Experimental results obtained by AlexNet-based FCN.
\r\n\tDiagnosis and management of complications while on ECMO therapy and weaning to recovery or advanced therapies will be also discussed.
\r\n\r\n\tChapters focusing on specific patient populations, such as cardiogenic shock, thoracic organ transplantation, trauma, and neonates, Covid-19 syndrome, will provide insight into the particular challenges in dealing with the unusual problems of these very diverse groups.
\r\n\r\n\tThe goal of this book is to provide, thanks to the thorough contributions by known experts in the field, a framework for successful program development. Hopefully, this text will also inspire others to further advance this delicate field.
",isbn:"978-1-80356-549-1",printIsbn:"978-1-80356-548-4",pdfIsbn:"978-1-80356-550-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"254c18981115aeda50bdf71829902141",bookSignature:"Dr. Antonio Loforte",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11718.jpg",keywords:"Heart Failure, Cardiogenic Shock, Respiratory Failure, Circulatory Failure, End-Organ Dysfunction, VA-ECMO, VV ECMO, Central ECMO, ECMO Running, Weaning off ECMO, Adverse Events While on ECMO, Survival on ECMO",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 10th 2022",dateEndSecondStepPublish:"April 7th 2022",dateEndThirdStepPublish:"June 6th 2022",dateEndFourthStepPublish:"August 25th 2022",dateEndFifthStepPublish:"October 24th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Loforte is a dedicated and pioneering researcher in the surgical treatment of advanced heart failure in terms of LVAD, BVAD, ECLS, and TAH adoption in different clinical scenarios. He is a member of several professional organizations including the prestigious STS, ISHLT, ASAIO, EACTS, RHICS, SICCH, SITO, ELSO, and ESOT among others. His bibliography lists over 150 peer-reviewed original articles, 250 abstracts (communications) for international meetings, 20 book chapters, and 8 manuals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"42172",title:"Dr.",name:"Antonio",middleName:null,surname:"Loforte",slug:"antonio-loforte",fullName:"Antonio Loforte",profilePictureURL:"https://mts.intechopen.com/storage/users/42172/images/system/42172.jpg",biography:"Dr. Loforte is currently staff surgeon and chair of the Mechanical Circulatory Support (MCS) program at the Department of Cardiothoracic, Transplantation and Vascular Surgery, S. Orsola Hospital, ALMA Mater Studiorum University of Bologna, IRCCS Bologna, Italy. 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The introduction of new materials, sensors, actuators, software, communications and use scenarios converted Robotics in a pushing area that embraces our everyday life. New robotic morphologies are the most shocking aspect that society perceives (i.e., the first models of each type generally produce the largest impact), but the long-term success of robotics is found in its capability to automate productive processes. Manufacturers and developers know that the market is found not only in large-scale companies (car manufacturers and electronics mainly) but also in the SME that provides solutions to problems that are manually performed so far. Also, robotics has opened the doors to new applications that did not exist some years ago and are also attractive to investors. These facts, together with lower prices for equipment, better programming and communication tools, and new fast-growing user-friendly collaborative robotic frameworks, have pushed robotics technology at the edge in many areas.
It is clear that industrial robotics leads the market worldwide, but social/gaming uses of robots have increased sales. Nevertheless, the most promising scenario for the present time and short term is the use of robots in commercial applications out of the plant floor. Emergency systems, inspection, and maintenance of facilities of any kind, rescues, surveillance, agriculture, fishing, border patrolling, and many other applications (without military use) attract users/clients because their use increases the productivity of the different sectors, low prices and high profitability are the keys.
There exist many robot morphologies and types (surface, underwater, aerial, underground, legged, wheels, caterpillar, etc.) but authors want to draw attention in the unmanned aerial vehicles (UAVs), which have several properties that make them attractive for a set of application that cannot be done with any other type of robot. First, those autonomous robots can fly, and therefore, they can reach areas that humans or other robots cannot. They are light, easy to move from one area to another, and can be adapted to any area, terrain, soil, building, or facility. The drawback is the fragility in front of adverse meteorological events, and their autonomy is quite limited compared with unmanned surface vehicles (USVs).
UAVs have seen the birth of a new era of unthinkable cheap, easy applications up to now. The authors would like to focus its use in the maintenance and inspection of industrial facilities, but specifically in the inspection of pipes in big, complex factories (mainly gas and oil companies) where the manual inspection (and even location and mapping) of pipes becomes an impossible task. Manned helicopters (with thermal engines) cannot fly close to pipes or even among a bunch of pipes. Scaffolds cannot be put up in complex, unstable, and fragile pipes to manually inspect them. Therefore, a complex problem can be solved through the use of UAVs for inspecting pipes of different diameters, colors, textures, and conditions in hazardous factories. This problem is not new and some solutions have been brought to an incipient market. Works as those in [1, 2] propose the creation of a map of the pipe set navigating among it with odometry and inertial units [3]. Obstacle avoidance in a crowded 3D world of pipes becomes of great interest when planning a flight; in [4], some contributions are made in this direction although the accuracy of object is deficient to be a reliable technology. Work in [5] overcomes some of the latter problems with the use of a big range of sensors, cameras, laser, barometer, ultrasound, and a computationally inefficient software scheme made the UAV too heavy and unreliable due to the excessive sensor fusion approach.
Many of the technical developments that have helped robotics grow have had a wider impact, especially those related with increasing computational power and parallelization levels. Faster processors, with tens of cores and additional multiple threat capabilities, and modern GPUs (graphics processing unit) have led to the emergence of GPGPU (general-purpose computing on GPU). These type of computing techniques have led to huge advances in the artificial intelligence (AI) field, producing the emergence of the “deep learning” field. The deep learning (DL) field is focused in using artificial neural networks (ANNs) that present tens or hundreds of layers, exploiting the huge parallelization capabilities of modern GPU. This is used in exploiting computational cores (e.g., CUDA cores), which compared on a one-to-one basis with a processor core, they are less powerful and slower, but can be found in amounts of hundreds or thousands. This has allowed the transition from shallow ANN to the deeper architectures and innovations such as several types of convolutional layers. In this work, the authors present a novel approach to detect pipes in industrial environments based in fully convolutional networks (FCNs). These will be used to extract the apparent contour of the pipes, replacing most of the architecture developed in [6] and discussed in Section 2. To properly train these networks, a custom dataset relevant to the domain is required, so the authors captured a dataset and developed an automatic label generation procedure base in previous works. Two different state-of-the-art semantic segmentation approaches were trained and evaluated with the standard metrics to prove the validity of the whole approach. Thus, in the following section, some generalities about the pipe detection and positioning problem are discussed, and the authors’ previous work [6] on it, as it will be relevant later. The next section discusses the semantic segmentation problem as a way to extract the apparent contour, both surveying classical methods, considered for earlier works, and state of the art deep-learning-based methodologies. The fourth section describes how the automatic label generator using multimodal data was derived and some features to the process. The experimental section starts discussing the metrics employed to validate the results, the particularities of the domain dataset generated and describes how an AlexNet FCN architecture was trained through transfer learning and the results achieved. To conclude, some discussion on the quality of the results and possible enhancements is introduced, discussing which would be the best strategies to follow continuing this research.
As it has been discussed, inspection and surveying are a frequent problem where UAV technologies are applied. The most common scenario found is that of a hard to reach infrastructure that is visually inspected through different sensors onboard a piloted UAV. Some projects have proposed the introduction of higher level perception and automation capacities, depending on the specific problem. In these cases, it is common to join state-of-the-art academic and industrial expertise to reach functional solutions.
In one of these projects, the specific challenge of accurately detecting and positioning a pipe in real time using only the hardware deployable in a small (per industry standards) UAV platform was considered (Figure 1), with several solutions studied and tested (including vision- and LIDAR-based techniques).
One of the UAV used for the development of perception tasks in the AEROARMS project. Several sensors were deployed, processing them with a set of SBCs (single-board computers), including a Velodyne LiDAR, two different cameras, ultrasonic range-finder (height), and optical flow.
In the case of LIDAR-based detection, finding a pipe is generally treated as a segmentation problem in the sensor space (using R3 data collected as “
With these results, discussed in [6], it was apparent that a new solution was needed, as the LiDAR approaches were too slow and the vision-based techniques probed themselves unreliable. The final proposed solution was based on integrating data from the laser and the vision sensors: the RANSAC over LiDAR approach would detect robustly the pipe and provide an initial position, which would then be projected into the image space (accounting for displacements if odometry is available) and used as a seed for the vision-based pipeline described.
In that same work [6], a sensibility analysis studying the effects of the relative pose between the sensor and pipes is provided. Once the pipe is detected in the LiDAR’s space sensor, the cylinder model is projected into the R2 image space using a projection matrix derived from the calibrated camera model (assumed to be a thin lens pinhole model, per classic literature [13]). This provides a region or band of interest where to look for the edges of the pipe in the image and is useful to solve the degenerate conic equation up to scale (i.e., being a function of the radius). An updated architecture version of the process is depicted in Figure 2.
The architecture of the multimodal perception pipeline combining LiDAR and camera vision. An updated version adds to previous works a validation step using odometric measurements.
The detailed architecture of the multimodal approach reveals how the LiDAR-based pipeline minimizes the data dimensionality by filtering non-curved surfaces (i.e., remove walls, floor, etc.) and also by removing entirely regions of the sensed space if priors or relevant data or the expected relative position of the pipe to the sensor is available. This was aimed at minimizing the size of the point cloud to be processed by the RANSAC step. To be able to project the detected pipe from the LiDAR sensor space into the camera image, some additional information was required: the rigid transformation between sensors (i.e., the calibration between LiDAR and camera) and an estimation of the odometry of the UAV. This is due because, even in the best assumption, with a performance slightly over 4 Hz, the delay between the captured point cloud and the produced estimation of the pipe would be over 200 ms. Therefore, the projection of the detected pipe to predict the area of interest to search the apparent contour has to consider the displacement during this period, not only the rigid LiDAR to camera transformation. This predicted region of interest is used in the vision process pipeline, with predictions of the appearance of the pipes into image space used to refine the contour search. This contour search relies on stacking a Hough transform to join line segment detector (LSD) detected segments (to overcome partial obstructions) on the relevant area and allows to choose the nearest correctly aligned lines. Notice that using a visual servoing library [14], an option to use data provided through human interaction was kept as available, though the integration of LiDAR detections as seeds into the visual pipeline made it unnecessary. To avoid degenerate or spurious solutions, a validation step (based on reprojection and “matching” of the Plückerian coordinates [15] for a tracked piped) was later introduced.
This architecture leads to a fast (limited by the performance of the vision-based part) and robust (based on the RANSAC resilience to spurious detections) pipe detector with great accuracy, which was deployed and test in a UAV. The main issue of the approach is the hardware requirements: access to odometry from the avionics systems, LiDAR, and camera sensors, and enough computing power to process them (beyond any other task required from the UAV). All this hardware is focused on solving what can be described as a semantic segmentation problem. This is relevant given the enormous changes produced in the last decade in the computer vision field, and how classic problems like semantic segmentation are currently solved.
In the context of computer vision, the semantic segmentation problem is used to determine which regions of an image present an object of a given category, that is, a class or label is assigned to a given area (be it a pixel, window, or segmented region). The different granularity accepted is produced by how the technique and its solution evolved: for a long time, it was completely unfeasible to produce pixel-wise solutions, so images were split according to different procedures, which added a complexity layer to the problem.
Current off-the-shelf technologies have changed the paradigm, as GPUs present huge capabilities in terms of parallelization, while solid-state disks make fast reliable storage cheap. These technical advancements have increased dramatically the performance, complexity, and memory available for data representation, especially for techniques inherently strong in highly parallelized environments. One of the fields where the impact has been more noticeable has been the artificial intelligence community, where the artificial neural network (ANN) has seen a resurgence thanks to the support this kind of hardware provides to otherwise computationally unfeasible techniques. The most impactful development in recent years has been the convolutional neural networks (CNNs), which have become the most popular computer vision approach for several of the classic problem and the default solution for semantic segmentation.
To understand the impact of deep learning into our proposed solution, we will discuss briefly how the classical segmentation pipeline worked and how the modern CNN-based classifier became the modern semantic segmentation techniques.
The classic semantic segmentation pipeline can be split into two generic blocks, namely image processing for feature extraction and feature level classification. The first block generally includes any image preprocessing done and resizing/resampling, splitting the image into the regions/windows, defining the granularity level of the classification, and finally, extracting the features itself. The features can be of any type and frequently the ones feed to the classification modules will be a composition of several individual features from different detectors. The use of different window/region-based approaches helps build up higher level features, and the classification can be refined at later stages with data from adjacent regions.
Notice that this kind of architecture generally relies on classifiers which required very accurate knowledge or a dataset with the classes to learn specified for each input so it can be trained. Figure 3 shows the detection of pipelines in classic semantic segmentation. Notice that to train the classifier, the image mask or classification result becomes also an input for the training process.
Block diagram of a classical architecture approach for semantic segmentation using computer vision.
So, it can be seen that solving the semantic segmentation problem though classic pattern recognition methods requires acute insight into the specifics of the problem domain, as the features to be detected and extracted are built/designed specifically. This implies (as mentioned earlier) working from low-level features and explicitly deriving the higher level features from them is a very complex problem itself, as they are affected by the input characteristics, what is to be found/discriminated, and which techniques will be used in the classification part of the pipeline.
Modern semantic segmentation techniques have organically evolved with the rise of the deep learning field to its current prominence. This evolution can be seen as a refinement in the scale of the inference produced from very coarse (image level probabilistic detection) to very fine (pixel level classification). The earliest ANN examples made probabilistic predictions about the presence of an object of a given class, that is, detection of objects with a probability assigned. The next step, achieved thanks to increased parallelization and network depth, was starting to tackle the localization problem, providing centroids and/or boxes for the detected classes (the use of
The first big break into the classification problem was done by AlexNet [13] in 2012, when it won the ILSVRC challenge, with a score of 84.6% in the top-5 accuracy test, while the next best score was only 73.8% (based on classic techniques). AlexNet has since then become a known standard and a default network architecture to test problems, as it is actually not very deep or complex (see Figure 4). It presents five convolutional layers, with max-pooling after the first two, three fully connected layers, and a ReLU to deal with non-linearities. This clear victory of the CNN-based approaches was validated next year by Oxford’s VGG16 [16], one of the several architectures presented, winning the ILSVRC challenge with a 92.7% score.
Diagram of the AlexNet architecture, showcasing its pioneering use of convolutional layers.
While several other networks have been presented with deeper architecture, relevant development focused on introducing new types of structures into the networks. GoogLeNet [17], the 2014 ILSVRC winner, achieved victory thanks to the novel contribution of the inception module, which validated the concept that the CNN layers of a network could operate in other orders different from the classic sequential approach. Another relevant contribution produced by technology giants was ResNet [18], which scored a win for Microsoft in 2016. The introduction of residual blocks allowed them to increase the depth to 152 layers while keeping initial data meaningful for training the deeper layers. These residual blocks architecture essentially forwards a copy of the received inputs of a layer; thus, later layers received the results and same inputs of prior layers and can learn from the residuals.
More recently, ReNet [19] architecture was used to extend recurrent neural networks (RNNs) to multidimensional inputs.
The jump from the classification problem with some spatial data to pixel level labeling (refining inference from image/region to pixel level) was presented by Long [20], with the fully convolutional network (FCN). The method they proposed was based on using the full classifier (like the ones just discussed) as layers in a convolutional network architecture. FCN architecture, and its derivatives like U-Net [21] are the best solutions to semantic segmentation for most domains. These derivatives may include classic methods, such as DeepLab’s [22] conditional random fields [23], which reinforces the inference from spatially distant dependencies, usually lost due to CNN spatial invariance. The latest promising contributions to the semantic segmentation problem are based on the encoder-decoder architecture, known as autoenconders, like for example SegNet [24].
For the works discussed in this chapter, a FCN16 model with AlexNet as a semantic segmentation model was used. The main innovation introduced by the general FCN was exploiting the classification power via convolution of the common semantic segmentation DL network, but at the same time, reversing the downsampling effect of the convolution operation itself. Taking AlexNet as an example, as seen in Figure 4, convolutional layers apply a filter like operation while reducing the size of the data forwarded to the next layer. This process allows producing more accurate “deep features” but at the same time also removes high-level information describing the spatial relation between the features found. Thus, in order to exploit the features from the deep layers while the keeping information from spatial relation, data from multiple layers has to be fused (with element-wise summation). In order to be able to produce this fusion, data from the deeper layers are upsampled using deconvolution. Notice that data from shallow layers will be coarser but contain more spatial information. Thus, up to three different levels can be processed through FCN, depending on the quantity of layers deconvoluted and fused, as seen in Figure 5.
Detail of the skip architectures (FCN32, FCN16, and FCN8) used to produce results with data from several layers to recover both deep features and spatial information from shallow layers (courtesy of [
More information on the detailed working of the different FCN models can be found in [25]. It is still worth noting that the more shallow layers are fused, the more accurate the model becomes, but according to the literature, the gain from FCN16 to FCN8 is minimal (below 2%).
Classic methods using trained classifiers would pick designed features (based on several metrics and detectors, as discussed earlier) to parametrize a given sample and assign a label. This would allow creating small specific datasets, which could be used to infer the knowledge to create bigger datasets in a posterior step. The high specificity of the features chosen (generally with expert domain knowledge applied implicitly) with respect to the task generally made them unsuitable to export learning to other domains.
By contrast, deep learning offers several transfer learning options. That is, as it was proven by Yosinski [26], trained with a distant domain dataset are generally useful for different domains and usually better than training from an initial random state. Notice that the transferability of features decreases with the difference between the previously trained task and the target one and implies that the network architecture is the same up to the transferred layers at least.
With this concept in mind, we decided to build a dataset to train an outdoor industrial pipe detector with pixel level annotation to be able to determine the position of the pipe. While the ability of transfer learning allows us to skip building a dataset with several tens of thousands of images, and therefore, the authors will work with a few thousand, which were used to fine-tune the network. These orders of magnitude are required as a “shallow” deep network. For instance, the AlexNet already presents 60 million parameters.
Capturing and labeling a dataset is a cumbersome task, so we also set to automatize this task with minimal human supervision/interaction, exploiting the capabilities of the sensing architecture proposed in earlier works described in Section 2.
This framework, see Figure 6, uses the images captured by the UAV camera sensor, the data processed by the localization approach chosen (see Section 2) to obtain the UAV odometry, and pipe detection seeds from the RANSAC technique treating the LiDAR point cloud data. When a pipe (or generally a cylinder) is detected and segmented in the data sensor provided by the LiDAR, this is used to produce a label for the temporally near images, to identify the region of the image (the set of pixels) containing the pipe or cylinder detected and its pose w.r.t. the camera. Notice that even running the perception part, the camera works at a higher rate than the LiDAR, so the full odometric estimation is used to interpolate between pipe detections, to estimate where the label should be projected into the in-between images (just as it was described for the pipe prediction in Section 2).
The framework proposed to automatically produce labeled datasets with the multimodal perception UAV.
This methodology was used to create an initial labeled dataset with actual data captured in real industrial scenarios during test and development flights, as it will be discussed in the next section.
To evaluate the viability of the proposed automated dataset generation methodology, we apply it to capture a dataset and train several semantic segmentation networks with it. To provide some quantitative quality measurement for the solutions produced, we use modified standard metrics for state-of-the-art deep learning, accounting that in our problem we are dealing with only one semantic class:
Notice that usually, besides the PA the mean pixel accuracy (MPA) is provided, but in our case, it reduces to the same value of PA, thus it will not be provided.
An additional metric usually computed along with the MIU is the frequency weighted MIU, which just weighs the average IoU computed at MIU according to the relative frequency of each class. The MIU, in our case, IoU is the most relevant metric and the most widely used when reporting segmentation results (semantic or otherwise).
The system proposed was implemented over the ROS meta-operating system, just as in previous works [6], where the UAV system used to capture the data is described. A set of real flights in simulated industry environments was performed, where flights around a pipe were done. During these flights, averaging ~240 s, an OTS USB camera was used to capture images (at 640 × 480 resolution), achieving an average frame rate of around 17 fps. This translated in around 20,000 raw images captured, including the parts of flight where no industry-like elements are present, thus of limited use.
Notice that as per the method described, the pipe to be found can be only labeled automatically when the LiDAR sensor can detect it; thus, the number of images was further reduced due to the range limitations of the LiDAR scanner. Other factors, such as vibrations and disruptions in the input or results of required perceptual data, further reduced the number of images with accurate labels.
Around ~2100 images were automatically labeled with a mask assigning a ground truth for the pipe in the image. After an initial human inspection of the assigned label, a further ~320 were rejected, obtaining a final set of 1750. The image rejected produced spurious ground truths/masks. Some of them had inconsistent data and the reprojection of the cylinder detected in through RANSAC in LiDAR scans was not properly aligned (error could be produced by spurious interpolation of poses, faulty synchronization data from the sensors, or due to deformation of the UAV frame, as it is impossible for it to be perfectly rigid). Another group presented partial detections (only one of the edges of the pipe is visible in the image), thus making it useless for the apparent contour optimization. A third type of error found was produced by the vision-based pipeline, where a spurious mask was generated, commonly some shadows/textures displace/retort the edge, or areas not pertaining to the pipe are assigned due similarity of the texture and complexity of delimiting the areas.
A sample of the labeling process can be seen in Figure 7, with the original image, the segmented pipe image, and approximations to centroid and bounding bow.
Left: dataset image. Middle: bounding box and centroid of the region detected. Right: segmentation mask image.
Out of the several options available to test the validity of the dataset produced, the shallow architecture AlexNet was selected, as it could be easily trained and it would provide some insight in the performance that could be realistically expected from a CNN-based approach deployed in the limited hardware of a UAV.
According to previous literature, the dataset was divided into training, validation, and test at the standard ratio of 70, 15, and 15% respectively.
To match the input of AlexNet the images were resized to 256 × 256 resolution. This was mainly done to reduce the computational load, as the input size could be easily fit adjusting some parameters, like the stride. To train and test the network, the Pytorch library was used, which provides full support for its own implementation of AlexNet.
To produce some metrics relevant to the network architecture just trained, a modified version of the technique used to label the dataset was used. Note that this approach, as described in previous sections, uses LiDAR, cameras, and odometry to: acquire an initial robust detection (from LiDAR), track its projection and predict it in the camera image space (using odometric data), and finally determine its edges/contour in the image. The robustness of the LiDAR detection is mainly due to exploiting prior knowledge (in the form of the known radius of the pipe to detect) that cannot be introduced into the AlexNet architecture to produce a meaningful comparison. So, a modified method, referred to as NPMD (no-priors multimodal detector) was employed to estimate the accuracy of earlier work detector without priors. The main difference was modifying the LiDAR pipeline to be able to detect several pipes with different radius (as it should be considered unknown). This led to the appearance of false positives and spurious measurements, which in turn weakened the results produced by the segmentation part of the visual pipeline.
Thus, FCN with AlexNet classification was trained using a pre-trained model for AlexNet, with the standard stochastic gradient descend (SGD) with a momentum of 0.9. A learning rate of 10−3 was used, according to known literature, with image batches of 20. The weight decay and bias learning rate were set to standard values of 5.10−4 and 2, respectively. Without any prior data, and no benefit to obtain by doing otherwise reported in any previous works, the classifier layer was set to 0, and the dropout layer in the AlexNet left unmodified. This trained model produced the results found in Table 1.
AlexNetFCN | UPMD | |
---|---|---|
PA | 73.4 | 56.7 |
IoU | 58.6 | 42.1 |
Experimental results obtained by AlexNet-based FCN.
It can be seen that eliminating the seed/prior data from the multimodal detector made it rather weak, with very low values for IoU, signaling the presence of spurious detections and probably fake positives. The FCN-based solution was around 1.5 times better segmenting the pipe, being a clear winner. This was to be expected as we deliberately removed one of the key factors contributing to the LiDAR-based RANSAC detection robustness, the radius priors, leading to the appearance of spurious detections.
It is worth noting that although the results are not that strong in terms of metrics achieved for a single-class case, there are no other vision-only pipe detectors with better results in the literature, neither other approaches actually tested in real UAV’s platforms, like authors’ previous works [6].
The field of computer vision has been greatly impacted by the advances in deep learning that have emerged in the last decade. This has allowed solving, with purely vision-based approaches, some problems that were considered unsolvable under this restriction. In the case presented, a detection and positioning problem, solved with limited hardware resources (onboard a UAV) in an industry-like uncontrolled scenario through a multimodal approach, has been solved with a vision-only approach. The previous multimodal approach relied in LiDAR, cameras, and odometric measurements (mainly from GPS and IMU) to extract data with complex algorithms like RANSAC and combine them to predict the position of a pipe and produce a measurement. This system was notable thanks to its robustness and performance but presented the huge requirements detailed in [6]. In order to solve the problem in a simpler and more affordable manner, a pure visual solution was chosen as the way to go, exploring the deep learning opportunities.
Although the switch to a pure visual solution meant that during its use, the procedure would only use the camera sensor, the multimodal approach was still used to capture data, and through a series of modifications, turn it into an automatic labeling tool. This allowed building a small but complete dataset with fully labeled images relevant to the problem that we were trying to solve. Finally, to test this dataset, we train a DL architecture able to solve the semantic segmentation problem. Thus, three different contributions have been discussed in this chapter: firstly, a dataset generator exploiting multimodal data captured by the perception system to be replaced has been designed and implemented; secondly, with this dataset generation tool, the data captured has been properly labeled so it can be used for DL applications; and finally, a sample lightweight network model for semantic segmentation, FCN with AlexNet classification, has been trained and evaluated to test the problem.
By the same reasons that there was no dataset available for our challenge and we had to capture and develop one dedicated to our domain, there were no related works to obtain metrics. In order to have some relevant metrics to compare the results of the developed approach, a modified version of [13] was produced and benchmarked without the use of prior knowledge. Under these assumptions, the new CNN-based method was able to clearly surpass the multimodal approach, though it still lacks robustness to be considered ready for industrial standards. Still, these initial tests have proven the viability of the built dataset generator and the utilization of CNN-based semantic segmentation to replace the multimodal approach.
This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness through Project 2016-78957-R.
There has been a global trend for populations to increasingly hold governments accountable to open government data (OGD) standards [1]. Because of this, governments have undertaken open data projects, such as providing public access to government data through publicly-accessible dashboards [2, 3]. However, government actors also may have an incentive to hide or obscure data, so there are barriers to accessing data for public dashboards [1]. This chapter focuses on the specific problem where governments attempt to demonstrate compliance with OGD standards through the presentation of a public dashboard, while at the same time, appearing to hide or obscure the data it is supposed to represent through poor dashboard design.
Our motivation to tackle this topic comes from our own disappointing experience trying to use a public dashboard implemented as part of OGD standards established where we live, in Massachusetts in the United States (US). Currently, in general, no standard guidance or recommendations are in place as a process to follow for the development of OGD public dashboards, and no framework or rubric has been proposed to evaluate them. These challenges are barriers to assessing how well public dashboards meet public need, and holding governments accountable for this. The significance of our contribution is that we propose a framework and rubric on which to base the evaluation of how well these public dashboards meet public need. The implication is that the application of this framework and rubric can be further researched in terms of utility in evaluating public dashboards. From this starting point, globally, we can begin to develop scientific consensus on what attributes in evaluate to a good-faith public dashboard implementation, and what the public should rightfully expect from the implementation of an OGD public dashboard.
The COVID-19 pandemic brought to attention a longstanding need for well-designed dashboards in public health and medicine [4]. It also brought to light that there are no uniform guiding principles behind developing publicly-facing dashboards intended to serve public interests. As a prime example, a recent review of United States (US) government public dashboards for COVID-19 found that “states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services” (from abstract) [4]. However, the results of this review do not address whether the public was adequately served by any of these dashboards that were funded with the public’s money. Important questions not answered were: Did these dashboards meet the public’s information needs? Did they meet the information needs of public health practitioners? Or more importantly – whose information needs were these dashboards supposed to meet, and what were these needs?
At present, there is no overarching philosophy behind public dashboard design, for public health or other topics [2]. Although individual projects will publish use-cases where they discuss their design philosophy [2, 5, 6], there has not been an overall effort by the professional informatics societies or other academic groups to assemble principles behind the design for dashboards intended to serve the public. This may be because such an effort would be daunting, and would require a relatively narrow scope. The scope should be aimed at addressing high-level requirements focused on ensuring that the public’s needs are met by whatever dashboard solution is developed, regardless of the topic.
This chapter will attempt to summarize the literature into a framework that provides a general, generic rubric by which to evaluate how well a dashboard design for the public ensures that the public’s needs are met through measuring their adherence to high-level requirements. The framework will also put forth a method by which to compare alternative dashboard solutions aimed at meeting similar public needs as to how consistent the solution is with the public’s dashboard requirements.
The framework and rubric are intended to evaluate outcomes. Logically, a design process that adequately includes the public that the dashboard is intended to serve will inevitably produce a dashboard solution that meets these outcomes. Hence, there is no need to invest public funding in bloated efforts such as the Rapid Cycle Quality Improvement (RCQI) model, which is promoted by many health departments and organizations, and is extremely paperwork intensive [7, 8]. Part of what causes the RCQI model to be so effort-intensive is that it measures process outcomes. By contrast, the evaluation framework for public dashboards recommended in this chapter is streamlined, and focused on achieving a design solution, not a process solution.
Nevertheless, an optimal design solution will not be achieved without an adequate design process. Therefore, it is important to consider how the public should be involved in the process of designing public dashboards – especially those that are publicly-funded, and therefore have obligations to respond to the public’s needs.
As stated previously, there is currently no agreed-upon best-practices design process for dashboards in general, and public dashboards specifically [2]. Each time a dashboard is developed, a different design process is used. But a generic, logical process can be summarized in Figure 1.
Generic logical dashboard design process. This design process produces an alpha prototype for initial testing, and a beta prototype for widespread field testing.
As shown in Figure 1, typically, before the dashboard is designed, some sort of design process is chosen, and this design process is followed to develop an “alpha prototype”. The alpha prototype represents a working mock-up that exists for the purposes of getting feedback and working out an initial design. Next, the alpha prototype undergoes a testing process to inform developers as to modifications that are necessary before widespread testing is done. Once those modifications are made, a beta prototype exists, and can be launched for field testing.
As described in Figure 1, depending upon the project, there can be different components included in the design process for the alpha prototype. First, there will be iterative design processes as part of designing the alpha prototype, as well as the development of design documentation and the actual creation of the prototype. The details behind each of these components will vary by project. Once the alpha prototype exists, the process to convert it to the beta prototype involves some sort of user testing, and some sort of evaluation for adherence to standards. Granted, an alpha prototype may be released into the field without having undergone the beta prototype process, but that means it has not been user-tested or evaluated for adhering to standards.
This logical process can apply to any dashboard development effort. As one example, researchers aimed to design a dashboard for clinicians [9]. They wrote requirements and developed an alpha prototype, then worked with clinicians to gain feedback to guide the development of a beta prototype (which would presumably be developed in the future and field-tested) [9]. This article focused mainly on the feedback process to improving the alpha prototype, but the focus of articles can be on any part of the dashboard design process. Another article focused on the development of a beta prototype aimed at both the public and leaders for real-time decision-making related to traffic flow [2]. While the beta prototype was developed and appeared ready for testing, the article did not report any results, so the current final stage of this project was not evident in the article [2].
Although, this logical design process should theoretically involve the intended users of the dashboard, and prototypes should undergo iterative testing, this is not always the case with public dashboards. Because public dashboards often involve government agencies and leaders at some level, whether as data sources or as intended audiences, these forces can have unintended impacts on the dashboard design and quality.
As a general trend, consumers are demanding more data transparency, and calls are being made for governments to make data available for public oversight [1]. Likewise, there is an increasing trend toward using dashboards for empowering the public [2, 3]. Not only do dashboards of public data provide a mechanism for public oversight of leaders, but they also reduce
However, governments are not always keen to share the data for various reasons. It has been argued that government agencies will be more likely to comply with open government data (OGD) practices if they see it as an opportunity to showcase their agency’s success [1]. However, if the agency believes the data will cast the agency in a negative light, the agency may be less likely to be inclined toward OGD practices. Ruijer and colleagues recommend that institutional incentives and pressure be created for OGD, because governments have a natural interest in suppressing data they think may be harmful to them in some way if analyzed [1].
However, data suppression is not the only method governments employ to prevent data use and interpretation. One limitation of legal requirements for OGD is that the agency may comply with the requirements in bad faith. During the COVID-19 outbreak in early 2020, a state epidemiologist in Florida said she was fired for refusing to manually falsify data behind a state dashboard [12]. Simply reviewing the limitations of big data can reveal ways to share big data in bad faith in a dashboard, such as visualizing too much data, visualizing incomprehensible or inappropriate data, and not visualizing needed data [13].
For this reason, in addition to holding governments to OGD standards, government efforts need to be evaluated as to whether or not they meet OGD standards
The evaluation framework presented has six principles on which to judge the level of good or bad faith in a public dashboard: 1) ease of access to the underlying data, 2) the transparency of the underlying data, 3) approach to data classification, 4) utility of comparison functions, 5) utility of navigation functions, and 6) utility of metrics presented. These principles will be described below.
A dashboard is essentially a front-end, with data behind it being visualized [14]. Hence, once a dashboard is published, members of the public may want to access the underlying data for various reasons, including oversight of the dashboard. But governments resistant to data-sharing may use the dashboard in bad faith as a firewall between the public and the underlying data to prevent data access [1]. Hence, good faith OGD principles hold that public dashboards should not serve as barriers, but instead serve as facilitators to access the underlying data being visualized in the dashboard.
Although raw data are used for the dashboard, in the dashboarding process, they undergo many transformations to be properly visually displayed [9, 14]. The processing of the data can develop calculations that are then displayed in the dashboard. Therefore, to be transparent, the dashboard must not only facilitate access to the underlying raw data, but also to the transformations the data underwent in being displayed. A simple way to accomplish this kind of transparency is to use open source tools and publish the code, along with documentation [14]. This allows citizen data scientists an opportunity to review and evaluate the decisions made in the dashboard display.
How data are classified in a dashboard can greatly impact the utility of the dashboard. As an example, developers of an emergency department (ED) dashboard that was in use for five years under beta testing found that after the ED experienced an outbreak of Middle East Respiratory Syndrome (MERS), major structural changes were needed to the dashboard [15]. Another paper about developing a visualization of patient histories for clinicians described in detail how each entity being displayed on the dashboard would be classified [16]. Hence, inappropriate classifications or ones deliberately made in bad faith can negatively impact data interpretation to the point that the dashboard could be incomprehensible to its users.
Dashboards are used to inform decision-making, and therefore, being able to make needed comparisons is an important factor in a dashboard’s usability [14, 17]. As an example, the public traffic dashboard described earlier presented visualizations of the ten most congested areas of the city, as well as textual feedback on the two most suitable routes between downtown and outlying areas, to provide optimal comparators to allow the public to make the most-informed route decision [2]. While ultimately, optimal design choices could be debated, it is easy to conceive of how agencies looking to maintain opacity could obscure data interpretation in a dashboard in bad faith by deliberately limiting the ability to make useful comparisons.
Dashboards are typically at least somewhat interactive, providing the user the ability to navigate through the data display, which responds to actions by the user [14, 18, 19]. When operating in good faith, developers often conduct extensive usability testing to ensure that the dashboard is intuitive to use in terms of navigating through the data display, and that any interactivity is useful [15]. But when implemented in bad faith, a dashboard could be designed to deliberately confuse the user as to how to navigate and interpret the data in the dashboard.
One of the main purposes of dashboards is to present metrics that represent statistics or visualizations meant to summarize a particular state of the data [14, 15]. For example, in the traffic dashboard, the metrics presented are intended to communicate traffic congestion to the users, while the metrics presented in the clinical dashboard are intended for healthcare workers to use in clinical decision-making [2, 16]. In a good-faith effort, developers may conduct extensive user-testing to ensure that the metrics presented are communicative to dashboard users, as is often done with dashboards developed to serve workers in healthcare [9, 20]. However, in a bad faith effort, the metrics presented could be deliberately confusing to the user, and serve merely to hide ugly truths in the underlying data.
In the US, the Commonwealth of Massachusetts (MA) Department of Public Health (DPH) posts annual healthcare-associated infection (HAI) reports about MA hospitals on their web page [21]. The purpose of the reports is public data transparency by law [21]. Briefly, HAI, such as catheter-acquired urinary tract infection (CAUTI), central line-associated bloodstream infections (CLABSI), and surgical site infections (SSI), are serious issues because they are the fault of the hospital, and can lead to sepsis, which is a systemic infection that can end in death [22, 23]. Since catheterization happens in the intensive care unit (ICU) setting, typically hospitals track CAUTI and CLABSI as part of healthcare quality activities centered around ICUs [14]. By contrast, SSIs are tracked in association with specific operative procedures (e.g., colon surgery) [24].
In the US compared to other countries, rates of HAI are relatively high, likely because they are not required to be tracked at the federal level [14, 25]. Hospitals can opt into the federal voluntary tracking system called the National Health Safety Network (NHSN), but the NHSN does not have a publicly-facing dashboard, and the data are inaccurate, especially in undercounting severe events [14, 24, 26]. As HAI is a serious public health issue, there has been a call for greater data transparency, so the reports posted on the MA DPH web site represent MA’s attempt to comply with state-level mandates for OGD.
Although summary reports are available for download on the MA DPH web site, it is not possible to access hospital-level reports directly from the web site. To download hospital-level reports, the user must access a dashboard presented on the web page in a link (Figure 2).
MA HAI public dashboard landing page. Note: “A” labels a menu of tabs that can be used for navigation to view metrics on the various hospital-acquired infections (HAIs). In panel labeled “B”, tabs can be used to toggle between viewing state-level metrics and hospital-level metrics. Hospitals can be selected for display using a map labeled “C”.
As per Figure 2, once inside the dashboard, individual PDF-style reports can be found through navigation to the hospital in question, and the reports appear to present formatted output from a database. One way to navigate to the hospital record is to locate it on the map (“C” in Figure 2) and click on its icon, causing the panel “B” to display hospital-level metrics and a link to the hospital’s PDF report.
Each PDF report has a header displaying attribute data about the hospital (e.g., number of beds), followed by a series of ICU-, procedure-, and infection-level output. This mirrors the structure seen in the dashboard tabs and reports (Figure 2, “A” and “B”). For the ICUs at each hospital, the report displays a set of tables summarizing CAUTI and CLABSI rates, followed by time-series graphs. For a set of high-risk surgical procedures, SSI rates and graphs for the hospital are displayed. Medication-resistant staphylococcus aureus (MRSA) and
The underlying data come from the NHSN. This is not stated on the dashboard. Instead, there is a summary report and presentation posted alongside the dashboard on the web site, and the analyses in these files are based on NHSN data [21]. It seems that the DPH is using this NHSN data using as a back-end to the dashboard, and the dashboard is an attempt to comply with OGD laws.
Because the authors are aware of the high rates of HAI in the US, and because we both live in MA and we both are women who are cognizant that sexism in US healthcare adds additional layers of risk to women [28], we identified that we were in a state of information asymmetry. Specifically, we had the
In this section, we start by evaluating the existing MA DPH HAI dashboard against our good vs. bad faith framework. Next, we propose an alternative dashboard solution that improves the good vs. bad faith features of the implementation.
Figure 3 provides a logical entity-relationship diagram (ERD) for the data behind the dashboard.
Logical entity-relationship diagram for data behind dashboard. Note: The schema presented assumes four entities: The hospital entity (primary key [PK]: HospRowID), each intensive care unit (ICU) attached to a hospital which contains the frequency of infection and catheter days attributes to allow rate calculation (PK: ICURowID), each procedure type attached to a hospital (to support the analysis of surgical site infection [SSI], with PK: ProcRowID), and each other infection type at the hospital not tracked with ICUs (PK: LabID).
As described earlier, the landing page (Figure 2) provides a map by which users can select a hospital, causing the metrics for the hospital to appear in a panel. The user chooses which measurement to view (e.g., CAUTI) through navigation using the tabs. This suggests the dashboard is aimed at individuals with a working knowledge of MA geography who are intending on comparing and selecting hospitals least likely to cause HAI for an elective procedure (e.g., childbearing), or to establish as their top choice of the local hospital should they ever need to be admitted. This interface makes it difficult to compare HAI at different hospitals, because metrics from more than one hospital cannot be viewed at the same time. Further, metrics about different HAIs at the same hospital are on different panels, so within-hospital comparisons cannot be facilitated. There appears to be no overall metric to use by which to compare hospitals in terms of their HAI rates.
Figure 4 shows an example of the metrics reported by each hospital on the dashboard reporting panel (“B” in Figure 2). The figure also shows one of the two tables and one of the two figures displayed on the CAUTI tab for the selected hospital. In all, two tables and two figures are displayed in portrait style in panel “B” (Figure 2), and Figure 4 shows the top table and figure displayed. In the table displayed (labeled “1” in Figure 4), the metrics presented are the number of infections, predicted infections, standard infection ratios (SIRS), a confidence interval for the SIR, and an interpretation of the level. The figure (labeled “2” in Figure 4) displays a time-series graph of SIRs for the past five years. In the other table on the panel (not shown in Figure 4), ICU-level metrics are provided about catheter-days, predicted catheter-days, Standard Utilization Ratios (SURs) and their confidence interval, and an interpretation, and an analogous time-series graph of five years of SURs is presented (also not shown in Figure 4).
Dashboard metric display for each hospital. Note: To view hospital-acquired infection (HAI) rates at hospitals, a hospital is selected (
SIRs and SURs are not metrics used typically by the public to understand rates of HAI in hospitals. Risk communication about rates to the public is typically done in the format of
Ultimately, the design process and requirements behind this dashboard are not known. There is no documentation as to how this dashboard was designed, and what it is supposed to do. It appears to be an alpha prototype that was launched without a stated
We chose to redesign the dashboard into a new alpha prototype that met requirements that we, as members of the public, delineated. Consistent with the good faith principles proposed, our requirements included the following: 1) the dataset we use should be easily downloadable by anyone using the dashboard, 2) the documentation of how the dashboard was developed should be easy to access, 3) hospitals should present summary metrics rather than stratified ones, 4) different HAI metrics for the same hospital should be presented together, and 5) there needs to be a way to easily compare hospitals and choose the least risky hospital. To do this, we first obtained the data underlying the original dashboard. Next, we analyzed it to determine better metrics to present. We also selected open-source software to use to redeploy an alpha prototype of a new dashboard. Finally, we conducted informal user testing on this alpha prototype.
Scraping was done in open-source
We chose to focus our inquiry on the data from the hospital and ICU tables, as CAUTI and CLABSI are by far the most prevalent and deadly HAIs [23]. Therefore, we scoped our alpha prototype to only display data from the ICU and the hospital tables (although we make all the data we scraped available in the downloadable dataset). This limited us to basing the dashboard on hospital- and ICU-level metrics only.
Next, we intended to present CAUTI and CLABSI frequencies and rates, whereby the numerator would be the number of infections, and the denominator would be the “number of patients catheterized”. We felt that the dashboard’s use of catheter-days as the rate denominator was confusing to the public, and appeared to attenuate the prevalence of patients having experienced a CAUTI or CLABSI. Although, “number of patients catheterized” was not available in the data, “annual admissions” was. Since the proportion of patients admitted annually who are catheterized probably does not vary much from hospital to hospital, we chose to use the number of admissions as the denominator and a proxy measurement.
Third, we needed to develop a way of sorting hospitals as to their likelihood of causing an HAI to allow easy comparisons by public users, so we decided to develop an equation to predict the likelihood of an HAI at the hospital. We did this by developing a linear regression model with hospital-level attributes as independent variables (IVs), and CAUTI rate in 2019 as the dependent variable (DV). We chose CAUTI over CLABSI after observing the two rates were highly correlated, and CAUTI was more prevalent.
Table 1 describes the candidate IVs for the linear regression model. The table also includes the source of external data that were added to the hospital data. We studied our IVs, and found serious collinearity among several variables, so we used principal component analysis (PCA) to help us make informed choices about parsimony [37]. The data predominantly loaded on three factors (not shown). The first factor included all the size and utilization variables for the hospital; these were summed into a Factor 1 score. The second-factor loadings included the proportion of those aged 65 and older and the non-urban flag (Table 1), so those were summed as Factor 2. Proportion non-White was strongly inversely correlated with Factor 2, so it was kept for the model, and county population did not load, so it was removed from the analysis. Factor 3 loadings included teaching status, for-profit status, and Medicare Performance Score (MPS). Rather than create a score, we simply chose to include the variable from Factor 3 that led to the best model fit to represent the factor, which was MPS. Then we finalized our linear regression model, and developed a predicted CAUTI rate (
Variable | Source | Role | Source |
---|---|---|---|
Teaching hospital status | Original dashboard | Exposure | Scraped data from dashboard |
Hospital profit status | Original dashboard | Confounder | Scraped data from dashboard |
Measurements of hospital size (number of beds) | Original dashboard | Confounder | Scraped data from dashboard |
Measurements of hospital utilization (number of admissions, number of patient days) | Original dashboard | Confounder | Scraped data from dashboard |
Medicare Performance Score | Medicare performance score dataset [34] | Confounder | Medicare performance score dataset |
Non-urban county | Rural health information hub [35], United States census [36] | Confounder | Scraped data from dashboard to determine county, then application of hospital rurality flag developed from public data based on county |
Hospital county population size | United States census [36] | Confounder | Census measurements |
Hospital county population proportion below the poverty level | United States Census [36] | Confounder | Census measurements |
Hospital county population proportion below the poverty level | United States Census [36] | Confounder | Census measurements |
Hospital county population proportion of non-White residents | United States Census [36] | Confounder | Census measurements |
Numerators for rates – infection frequencies | Original dashboard | Create outcome | Scraped data from dashboard |
Infection rates | Original dashboard | Outcome | Calculated as the numerator for rates divided by admissions |
Conceptual model specification.
Next, we used the regression equation to calculate
Once the lethality score was calculated, we chose to sort the hospitals by score, and divide them into four categories: least probable (color-coded green), somewhat probable (color-coded yellow), more probable (color-coded red), and most probable (color-coded dark gray). Due to missing CAUTI information and many hospitals having zero CAUTI cases, our data were severely skewed left, so making quartiles of the lethality score to divide the hospitals into four categories was not meaningful. To compensate, we sorted the data by lethality score and placed the first 23 hospitals (32%), which included all the hospitals with zero cases, in the least probable category. We placed the next 16 (23%) in somewhat probable, the next 16 (23%) in more probable, and the final 16 (23%) are most probable. We chose to use this classification in data display on the dashboard to allow for easy comparison between hospitals of risk of a patient contracting HAI.
R is an open-source analytics software that allows for user-developed “packages” to be added
The package
The dashboard we developed was deployed on a server (https://natasha-dukach.shinyapps.io/healthcare-associated-infections-reports/) and code for the dashboard was published (https://github.com/NatashaDukach/HAI-MA-2019). When accessing the link to the dashboard, the user initially sees a map with icons (in the form of dots) on it indicating hospitals. The icons are color-coded according to the lethality score described previously. Clicking on an icon will expand a bubble reporting information about the hospital (Figure 5).
Alternative dashboard solution. Note: In our new version, two tabs are created (see “A”). The figure shows the first tab titled “ICU Rate Explorer”. The second tab, titled “data collection”, has information about the design of the dashboard and links to the original code. Each of the hospitals is indicated on the map by a color-coded icon that can be clicked on to display a bubble. The legend by “B” displays our color-coding scheme. When clicking on a hospital icon, hospital-level metrics are shown in a bubble, and there is a link that leads to the display of intensive care unit (ICU)-level metrics (see “C”).
As shown in Figure 5, like the original dashboard, this one has a map for navigation. Unlike the original, it only has two tabs: “ICU Rate Explorer” (the one shown in Figure 5), and “Data Collection”, which provides documentation and links to original data and code (see “A” in Figure 5). The hospital icons are placed on the map and coded according to our color scheme (see legend in Figure 5 by “B”). This allows for easy comparison between hospitals. When clicking on an icon for a hospital, a bubble appears that contains the following hospital metrics: Number of admissions, number of ICU beds, overall CAUTI rate, and overall CLABSI rate. There is also a link on the bubble where the user can click to open a new box that provides CAUTI and CLABSI rates stratified by ICU. Future development plans include adding other overall rates (e.g., for SSI), and adding in data from previous years to allow for the evaluation of trends.
Informally, members of two potential user bases were queried as to their reactions to the differences between the two dashboards: members of the academic public health space, and members of the MA public. When the dashboard redesign was pitched as a project to public health academics, it was dismissed as an unimportant escapade for various reasons. Some reasons cited were lack of agreement on terminology and patient safety priorities, the challenges with undercount of HAIs in NHSN data, and differential reporting accuracy in teaching vs. non-teaching hospitals. Academics also acknowledged that the system for tracking, addressing, and preventing HAIs is hopelessly broken in the US, and therefore it seems a waste of time to prop up such a system when it produces inaccurate data.
A few members of the MA public who are familiar with technology also provided informal feedback about the utility of the dashboard from a patient standpoint. They reported that the alternative solution was more intuitive than the original, and did a better job of representing the highly limited data from the NHSN.
These differences in reactions underscore the challenge of OGD and ensuring that public dashboards are developed and deployed in good faith. Those from the public health field expressed that since the system is broken and the data are inaccurate, they should be dismissed, while those in the public felt that since the data existed, it should be accessible, even if it was not completely accurate. It not only highlights the differing perspectives of those on either side of information asymmetry, it glaringly illustrates how those who are being held accountable by the usage of the data see dashboarding differently than those who are using the data to do oversight and accountability.
We wanted to compare the original HAI dashboard with the one we developed based on the good faith principles described earlier. We started by creating the framework presented in Table 2, which guides as to the good faith and bad faith characteristics of public dashboards.
Dashboard function/characteristic | Good faith | Bad faith |
---|---|---|
Access to underlying data |
|
|
Transparency of underlying data |
|
|
Data classification | Data are classified in ways that are intuitive to the consumer, and results are presented according to those classifications |
|
Comparison functions | Dashboard allows for comparisons that provide useful consumer decision-support | Dashboard prevents comparisons that would provide consumer decision-support, or makes them very difficult to make using the dashboard functions |
Navigation functions |
|
|
Metrics presented |
|
|
Proposed framework for evaluation of good faith and bad faith public dashboards.
Using this framework, we applied a rating system. We chose zero to represent “neither bad faith nor good faith”, −5 to represent “mostly bad faith” and + 5 to represent “mostly good faith”. Then, based on our experience and available information, we rated the original MA HAI dashboard and our alternative dashboard solution to compare the ratings. To experiment with applying our framework to another public dashboard, we used the information published in the article described earlier to rate the traffic dashboard which was in Rio de Janeiro [2]. Our ratings appear in Table 3.
Dashboard function/characteristic | MA Hosp | Alt. MA Hosp | Comment: MA Hosp vs. Alt. MA Hosp | Rio | Comment: MA Hosp vs. Rio |
---|---|---|---|---|---|
ACCESS TO UNDERLYING DATA | −5 | 5 | The original solution had no access to underlying data (except by way of PDF-style reports). Alternative solution posts data publicly for download. | 5 | Rio dashboard uses open data from City Hall with user-generated content collected through Waze |
Transparency of Underlying Data | −5 | 3 | It was difficult to identify the source of the data in the original solution. The alternative solution uses the same data, which is from NHSN. Because NHSN itself is somewhat opaque, the final solution lacks transparency. | 0 | Unclear from the article, but it appears that it is possible to audit Rio dashboard design if a member of a certain role (e.g., public data scientist). Not all tools used were open source. |
Data Classification | 0 | 3 | In informal user testing, public users found the data classifications much more intuitive in the alternative compared to the original solution. However, formal user testing was not conducted. | 5 | Much effort was made to classify data in Rio dashboard to make it useful for the public to make route decisions. |
Comparison Functions | −5 | 5 | In informal user testing, users found the comparison function in the alternative solution useful for decision-making, and could not find a comparison function in the original solution. | 5 | Rio dashboard was designed to allow the public to make comparisons about potential traffic routes. |
Navigation Functions | 0 | 5 | In informal user testing, users reported being able to easily navigate the data and dashboard in the alternative solution, but having extreme difficulty in navigating the original solution. | 5 | Rio dashboard for the public had a very simple, intuitive interface with images and only a few metrics critical to decision-making. This made it possible to easily navigate the dashboard display. |
Metrics Presented | −5 | 5 | In informal testing, users indicated that they did not understand the metrics presented on the original solution but found the color-coding of the alternative solution intuitive for decision-making. | 3 | The few metrics presented on the Rio dashboard were geared specifically to helping the public make route decisions based on traffic metrics. However, no formal user testing is presented. |
Total | −20 | 26 | 23 |
Application of rating system.
Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, Rio = Rio traffic dashboard [2], and NHSN = National Healthcare Safety Network.
As shown in Table 3, using Table 2 as a rubric and our rating scale, we were able to rate each dashboard and assign a score. We were also able to define in the comments in the table the evidence on which we based our score. Table 3 demonstrates that this framework can be used to compare two different alternatives of a public dashboard displaying the same data, as well as two completely different public dashboards. The total scores show that while our redesigned prototype of the HAI dashboard had a similar level of good faith implementation compared to the Rio traffic dashboard (scores 26 vs. 23, respectively), the original HAI dashboard had a very low level of good faith implementation compared to the other two (score − 20).
As is consistent with the global trend, the state of MA implemented an OGD requirement to share HAI data with the public through posting a public dashboard on a web page. However, as residents of MA, the authors found that this dashboard did not serve our information needs, and essentially obscured the data it was supposed to present. To address this challenge, we not only redesigned the dashboard into a new prototype, but we also tested our proposed framework for evaluating the level of good faith in public dashboards by applying it. Using our proposed framework and rubric, we evaluated the original HAI dashboard, our redesigned prototype, and a public dashboard on another topic presented in the scientific literature on the level of good faith implementation. Through this exercise, we demonstrated that the proposed framework is reasonable to use when evaluating the level of good faith in a public dashboard.
The next step in the pursuit of holding governments accountable for meeting OGD standards in public dashboards is to improve upon this framework and rubric through rigorous research. As part of this research, entire groups of individuals could be asked to score dashboards on each of these characteristics, and the results could easily be summarized to allow an evidence-based comparison between dashboards. Results can be easily visualized in a dumbbell plot (using packages
Example of visualization of framework score comparison. Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, and Rio = Rio traffic dashboard [
As visualized in Figure 5 and summed in Table 3, our scoring system suggested that the alternative HAI dashboard we developed was done in a level of good faith (score = 26) similar to that of the Rio traffic dashboard (score = 23), and that the original HAI dashboard appears to not have been done in good faith (score = −20), and may serve as a governmental attempt to hide or obscure uncomfortable data. This exercise shows that the framework and rubric developed can be used to compare the level of good faith in public dashboards, and to provide evidence-based recommendations on how governments can improve them so they meet both the spirit and the letter of OGD requirements.
In conclusion, in this chapter, we describe the challenge of holding governments accountable for developing public dashboards to meet OGD requirements in a way that also serves the public’s information needs. To address this challenge, we propose a framework of six principles of good faith OGD by which public dashboards could be evaluated to ensure data shared by the government under OGD policies and laws are done so in good faith. We also demonstrate applying this framework to the use-case of a public dashboard intended for residents of MA in the US to use to compare and select hospitals based on their HAI rates. As a demonstration, we present our redesign of the dashboard, then use a rubric based on the framework to score and compare the original dashboard and our alternative in terms of levels of good faith OGD. We also demonstrate using the rubric on a published use-case in the literature. As our framework and rubric provide a reasonable starting point as a method for evaluating and comparing the level of good faith in public dashboards, we strongly recommend that future research into this topic consider our framework and rubric, and build upon it through gathering evidence in the field.
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\n\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
\n\nA Conflict of Interest can be identified at different phases of the publishing process.
\n\nIntechOpen requires:
\n\nCONFLICT OF INTEREST - AUTHOR
\n\nAll Authors are obliged to declare every existing or potential Conflict of Interest, including financial or personal factors, as well as any relationship which could influence their scientific work. Authors must declare Conflicts of Interest at the time of manuscript submission, although they may exceptionally do so at any point during manuscript review. For jointly prepared manuscripts, the corresponding Author is obliged to declare potential Conflicts of Interest of any other Authors who have contributed to the manuscript.
\n\nCONFLICT OF INTEREST – ACADEMIC EDITOR
\n\nEditors can also have Conflicts of Interest. Editors are expected to maintain the highest standards of conduct, which are outlined in our Best Practice Guidelines (templates for Best Practice Guidelines). Among other obligations, it is essential that Editors make transparent declarations of any possible Conflicts of Interest that they might have.
\n\nAvoidance Measures for Academic Editors of Conflicts of Interest:
\n\nFor manuscripts submitted by the Academic Editor (or a scientific advisor), an appropriate person will be appointed to handle and evaluate the manuscript. The appointed handling Editor's identity will not be disclosed to the Author in order to maintain impartiality and anonymity of the review.
\n\nIf a manuscript is submitted by an Author who is a member of an Academic Editor's family or is personally or professionally related to the Academic Editor in any way, either as a friend, colleague, student or mentor, the work will be handled by a different Academic Editor who is not in any way connected to the Author.
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\n\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
\n\nEXAMPLES OF CONFLICTS OF INTEREST:
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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Dual-beam platforms, combining a high-resolution scanning electron microscope (HR-SEM) and an FIB column, additionally equipped with precursor-based gas injection systems (GIS), micromanipulators, and chemical analysis tools (such as energy-dispersive spectra (EDS) or wavelength-dispersive spectra (WDS)), serve as multifunctional tools for direct lithography in terms of nano-machining and nano-prototyping, while advanced specimen preparation for transmission electron microscopy (TEM) can practically be carried out with ultrahigh precision. Especially, when hard materials and material systems with hard substrates are concerned, FIB is the only technique for site-specific micro- and nanostructuring. Moreover, FIB sectioning and sampling techniques are frequently used for revealing the structural and morphological distribution of material systems with three-dimensional (3D) network at micro-/nanoscale.This book chapter includes many examples on conventional and novel processes of FIB technologies, ranging from analysis of semiconductors to electron tomography-based imaging of hard materials such as nanoporous ceramics and composites. 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Electron diffraction patterns are used to obtain quantitative data including phase identification, orientation relationship and crystal defects in materials, etc. At first, a general introduction including a geometrical and quantitative approach to electron diffraction from a crystalline specimen, the reciprocal lattice and electron diffraction in the electron microscope are presented. The scattering process by an individual atom as well as a crystal, the Bragg law, Laue conditions and structure factor are also discussed. Types of diffraction patterns such as ring pattern, spot pattern and Kikuchi pattern, and general and unique indexing diffraction patterns are explained. The procedure for indexing simple, complicated and imperfect patterns as well as Kikuchi lines and a combination of Kikuchi lines and spots is outlined. The known and unknown materials are identified by indexing patterns. Practical comparisons between various methods of analysing diffraction patterns are also described. The basic diffraction patterns and the fine structure in the patterns including specimen tilting experiments, orientation relationship determination, phase identification, twinning, second phases, crystallographic information, dislocation, preferred orientation and texture, extra spots and streaks are described in detail. Finally, electron diffraction patterns of new materials are investigated.",book:{id:"5075",slug:"modern-electron-microscopy-in-physical-and-life-sciences",title:"Modern Electron Microscopy in Physical and Life Sciences",fullTitle:"Modern Electron Microscopy in Physical and Life Sciences"},signatures:"Mohsen Asadi Asadabad and Mohammad Jafari Eskandari",authors:[{id:"176352",title:"Dr.",name:"Mohsen",middleName:null,surname:"Asadi Asadabad",slug:"mohsen-asadi-asadabad",fullName:"Mohsen Asadi Asadabad"},{id:"177600",title:"Dr.",name:"Mohammad",middleName:null,surname:"Jafari Eskandari",slug:"mohammad-jafari-eskandari",fullName:"Mohammad Jafari Eskandari"}]},{id:"38543",title:"Application of FTIR Spectroscopy in Environmental Studies",slug:"application-of-ftir-spectroscopy-in-environmental-studies",totalDownloads:27659,totalCrossrefCites:10,totalDimensionsCites:42,abstract:null,book:{id:"2397",slug:"advanced-aspects-of-spectroscopy",title:"Advanced Aspects of Spectroscopy",fullTitle:"Advanced Aspects of Spectroscopy"},signatures:"Claudia Maria Simonescu",authors:[{id:"142381",title:"Dr.",name:"Claudia Maria",middleName:null,surname:"Simonescu",slug:"claudia-maria-simonescu",fullName:"Claudia Maria Simonescu"}]}],onlineFirstChaptersFilter:{topicId:"228",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11404,editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",slug:"adriano-andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",biography:"Dr. Adriano de Oliveira Andrade graduated in Electrical Engineering at the Federal University of Goiás (Brazil) in 1997. He received his MSc and PhD in Biomedical Engineering respectively from the Federal University of Uberlândia (UFU, Brazil) in 2000 and from the University of Reading (UK) in 2005. He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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