Common functionalities of RSI software.
\r\n\t
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"40526",title:"Raster and Vector Integration for Fuzzy Vector Information Representation Within GIS",doi:"10.5772/51413",slug:"raster-and-vector-integration-for-fuzzy-vector-information-representation-within-gis",body:'This chapter deals with a vector model for fuzzy data sets within a GIS and with the raster and vector combination to refine vector objects. The model will be useful to represent information such as natural phenomenon (forest, desert, geology, separation between mountain and valley, rain, inundation, wind, etc.), social phenomenon (population density changes, poverty, etc.) or physical phenomenon (hurricane, etc.), which are diffused in space (Guesgen, 2000). The use of raster information will be useful to split vector units when the vector division reaches its limits.
The main sources of raster data are raw images (airborne and satellite images) and results of treatments (geostatistical, pixel classification, etc.). Vector information is often obtained by manual measurements (using GPS receptor for example), or is the result of the vectorisation of a raster treatment (such as classification, etc.).
The main goal is to manipulate vector information instead of raster because their manipulation within GIS engenders many problems. Indeed, the raster data are not adapted to GIS treatment (Benz, 2004) because of a lack of contextual information, the size of the data and the time consuming algorithm to produce information. The raster information is not split into identified objects and the vector representation is more flexible and gives the possibility to be easily combined with other information layers. For these reasons, we aim to split a wide image into several small units and convert the raw image information into a vector of features characterizing the unit in a raster view.
Another observation is that the vector data structures are not adapted to model fuzzy information in GIS (Shneider, 1999). More than 10 years later, the only way to use fuzzy representation in GIS is to build a raster map (Bjorke, 2004), computed with different raster or vector sources (Kimfung, 2009), (Ruiz, 2007). The only approach dealing with fuzzy vector representation uses a series of regular buffers inside and outside a polygon boundary to represent different belts of membership function values (Lewinski, 2004). The main drawback of this approach is that for most of the natural phenomena that engender fuzzy data (such as forest, population movement, physical phenomena like hurricane, etc.) the membership function doesn’t have the same behaviour in each spatial direction.
We propose in this chapter a vector model adapted to fuzzy information without the previous limitations. The chapter is organized in XX sections including this introduction. Section 2 details the fuzzy vector representation by successively introducing
In 1997, in the conclusions of the special issue of
Historically the first approaches to solve the problem were to arbitrary decide of a strict border between fuzzy sets and to model them with classical data structures. But the raise of more complex problematics integrating many parameters reached the limits of this model which have guided the conception of the datastructures within the GIS.
The first studies about 2D fuzzy sets have been made by Peter Burrough in 1986 (Burrough, 1986) 106. After this, many studies with the definition of 2D fuzzy operators or fuzzy spatial relation (Bjorke, 2004), (Kimfung, 2009) have been made. But all this studies are using a raster representation of the data (Zhu, 2001), (Mukhopadhyay, 2002), (Sunila, 2004), (Bjorke, 2004), (Guo, 2004), (Ruiz, 2007), (Sawatzky, 2008), (Sunila, 2009), (Gary, 2010), (Wolfgang, 2011) and it was still the same in 2010. In all this studies it was s necessary to rasterise the data in order to apply the fuzzy operators. This implies a loss of precision when choosing a scale of analysis, a time consuming process and the lack of fuzzy representation of sources data. This last point has been underline by GIS community as a main drawback (Altman, 1994), (Shneider, 1999), (Fisher, 2000), (Cross, 2001), (Yanar, 2004), (Kainz, 2011).
The actual challenge is then to directly deal with the vector data in order to improve precision, reduce computation time and to ensure a better abstraction of the data. Nowadays, only marginal studies include a vectorial approach to fuzzy spatial problems (Benz, 2004) (karimi, 2008). These approaches consist on a series of regular buffers around a strict polygon to represent different level of the membership function values. The main drawback of these approaches is that the regular evolution of the fuzzy membership function in each direction doesn’t translate the reality of most of the observed phenomenon.
In the study case, used to illustrate this chapter, we built a fuzzy representation of different forest in Guadeloupe Island. A forest is typically the kind of data adapted to the modelling with fuzzy sets. Indeed, the transition between two kinds of forest is mostly a gradient than a strict transition. Moreover, the gradient depends on many parameters and could be relatively short if environmental conditions change quickly or long if there is a smoother change. In some particular conditions there is a strict border for a forest, for example at the interface with agriculture or if a road, river, crest etc. interact with the forest. In any case, the transition gradient is locally defined and not uniform in every direction.
The classes are semantically and numerically defined using floristic information collected over 47 areas (about 250 m² each, Fig. 1-a). This step is based on a Principal Component Analysis (PCA, Fig. 1-b) and an ascending hierarchical clustering method (AHC, Fig. 1-c). These first steps allow regrouping the floristic information into significant clusters by sorting the numerous parameters describing the 47 areas. The AHC is used to select the number of classes and also serves as base for the semantic definition of the classes given through an ontology (Fig. 1-d) (Jones, 2002), (Kavouras, 2005), (Fonseca, 2002, 2006,2008), (Baglioni, 2008), (Gutierrez, 2006). The ontology is useful for a high level use of the fuzzy representation of the forest and particularly when integrating it to a shared conceptual layer (Eigenhofer, 1991), (Cruz, 2005), (Bloch, 2006), (Grandchamp, 2011).
Clustering on floristic data and class definition
This step allows defining 14 kinds of forest over the main part of Guadeloupe Island (Fig. 1-d and e).
The previous step allows labelling each of the 47 areas according to the 14 classes (Fig. 2-a). But the fuzzy representation of the whole territory is not possible using floristic information because we didn’t have this information for any other area. Under the hypothesis that the environment directly influences the formation of the forests we project the 47 areas in a topographic and environmental space. This space includes information such as general ground occupation, elevation, exposition, slope, humidity or latitude. Each of this data is stored within a vector information layer and their fusion leads to the division of the territory into elementary areas called Vector Units (VU, Fig. 2-b). Each of the 47 ground truth areas is contained in a VU.
Learning on topographic and image data based on decision tree
Each VU represents a uniform area regarding each of the fused layers and we add to these features information extracted from a raster view (Raster Unit RU) of the area (texture and colour characterization using co-occurrence matrices, Law filters, Gabor filters, Hue moments, fractal dimension, etc.). We use satellite images (IKONOS, Spot5 and Quickbird) and airborne images. A total of about 20 features including topographic and image features are used.
This step of characterisation of the VU is the first Raster-Vector cooperation. It includes a partition of the image according to heterogeneous vector data and not to spectral or structural properties of the image. This approach is more simple and quicker than image segmentation and the returned VU have a semantic signification. Moreover, the adjunction of raster and vector information allows combining a theoretical uniformity of an area and a raster view of the reality.
With these 47 labelled VU we are now able to classify the whole territory in a fuzzy way. Indeed, we apply a supervised classification based on a decision tree (Fig. 2-b) to obtain the different kind of forest.
The decision tree is built after a learning step based on both topographic and image features. We use different kind of decision trees such as functional decision trees (FT) (Gama, 2005) and C4.5 decision tree (Quinla, 1993). The best results were statistically obtained with FT, so we keep this approach to illustrate the method in the rest of the chapter.
The fuzzy model requires the computation of a membership function for each elementary unit (VU). This membership function is derived from the reliable coefficient returned by the decision tree. Indeed, each VU is analysed using the FT and a reliable coefficient is returned for each class. Commonly a strict classification using DT will choose to label the VU with the label of the class having the highest value of reliable coefficient.
By retaining only the highest value, we totally ignore the gradient nature of the transitions. By keeping all values we are able to build a fuzzy representation of each class and different representations of the resulting map by defining rules for the transitions. Moreover, in case of wide transition area, this approach could be used to reveal full transition classes.
Fig. 3 shows the membership function of each VU to different classes among the 14 identified classes of Guadeloupean forest. The lower the membership function value is, the darker is the colour. We remark that some classes are clearly localised, such as class 9 which represents high mountain forest around the sulphur mine, or class number 4 which is a typical forest kind oriented to the west and where a dry hot wind is blowing or else class 1. But other classes are more dispersed over the whole territory. This reveals wide transitions between classes. We remind that these maps are not raster data but each coloured element is a VU.
Membership function
Now we have all necessary information to build the fuzzy model of the forest. The simplest model is to store the vector of the membership degree values in each VU. This is also the more precise model because we don’t lose any data. But this model is not easily useful. In order to simplify the representation of the different transition gradients we decide to build different belts of membership degree values for each class. The spatial and topologic information used in the fuzzy classification process ensure the spatial coherence and compactness of the membership degree values. The models will differ from the number of belts and also the value of the threshold between the belts. These values will influence the treatments in two ways:
So we will now see different ways to set the belts. Fig. 4 and Fig. 5 show different fuzzy representations of the class number 9. The differences are linked to the number of belts (5 for
Different fuzzy vectorial respresentation of a class 9 with 5 buffers
Different fuzzy vectorial respresentation of class 9 with 10 buffers
The choice of the number and positions of the belts is made by the user depending on the application. These values could be set manually or automatically computed using rules such as: natural breaks, equal intervals, geometrical interval, standard deviation, etc. This fuzzy data structure allows a fine and faithful representation of the heterogeneous evolution of the class in each direction. Indeed this model translate this property because the heterogeneity of the different information layers used (resulting on VU with totally different shapes) leads to a non uniform evolution of the membership degrees in each direction.
Moreover, the choice of the thresholds influences the topology of the model and some resulting belts could be a unique connected polygon or on contrary a set of disjointed polygons.
The only way to validate the model is to compare it to an existing model. However there isn’t any fuzzy model of the studied classes. So in order to validate the model we are going to compare a strict view of the model with an existing strict classification of the concerned forests. The referred classification is an ecological map obtained manually by biologists in 1996 (Rousteau, 1996).
Fig. 6 shows in left the reference map and in right the map obtained with a conversion of the fuzzy map into a strict map. The conversion has been made in the simplest way by attributing to each VU the class label of the class having the highest membership degree value. Areas in white in the map are not taken into account in the classification. The main confusion is made between classes 9 and 12 in the east part of the Island.
Strict classification comparison
Fig. 7 shows a zoom of the map on a particularly complex area: the National parc. We observe the similarity of the two maps with a localisation of the forest at the attended places. Fig. 7 -c) shows the differences (inblack) between the ecologic map and the strict classification. The differences are localized at the limits between each kind of forest and this is exactly what is criticable in a strict classification of diffuse data. Taking into account that the map on the left (Fig. 7 -a) has been made manually with arbitrary decision concerning the limits of the forest and that the map in the middle (Fig. 7 -b) is the result of a complex and complete modelling, learning and classification process we can estimate that the results are of good quality.
Resulting strict classification zoomed on Natural parc of Guadeloupe
Now we are interested on the transition between classes and want to focus on wide transition area in order to eventually decide to create transition classes. Fig. 8 shows different transitions between classes. We display in black all areas were the membership degree has a value under a fixed threshold. So in the first map, only few areas have a reliable coefficient under 0.5, and in the third map more areas but relatively few are under 0.9 or 0.95. These results tend to show that this uncertainty is relative low concerning the classes and that the transition gradient is high, that is to say that we quickly go from one forest to another.
We also observe that the width of the transition is not the same between every forest (for example compare transition between blue and green classes and between red and orange classes). This illustrates the good and attended property of the model which should translate the reality of the transition which is heterogeneous.
Full transition classes
This first part of the chapter shows a new fuzzy model to represent data having diffuse and uncertain limits. The advantage of this model is that (i) it doesn’t require a rasterisation of the data which implies a loss of precision (ii) it doesn’t treat every transition in the same way and allows different transition width (iii) the model is enough flexible to be adapted for different applications. The building of the model uses a raster-vector cooperation to define the features during the classification process but the model is based on a vector reperesentation.
It has been successfuly apply to forest modelling and classification and gives results close to a reference manual classification made by biologists.
This step aims to refine the Vector Units (VU) by using the Raster information computed on each Raster Unit (RU) separately (Grandchamp, 2009), (Grandchamp, 2010).
At this step, we consider that the VU couldn’t be refined using external vector information. This could be the case if all vector layers have been used to produce the VU or if the adjunction of any other vector information haven’t any sense (for semantic reasons or else) (Gahegan, 2008). For example if we split environmental VU with administrative borders. We also remind that a VU is a uniform area according to some specific vector information and that we expect that the VU will also have a uniform visual aspect. If not, or if we are looking for some specific area within a VU we have to split it into sub-VU.
So the only way to split the VU into sub-VU is to use the Raster information (satellite images, airborne images, etc.). Let us now consider two cases leading to the same process. Firstly we are looking for some specific objects within a VU and we hope their spectral or structural information is enough discriminating to detect them. Secondly we detect a non homogenous VU (regarding criterions detailed later) and we hope the homogenous sub-areas are semantically significant. In both cases, we make the hypothesis that the spectral or structural information contained in the raster view of the unit is helpful to split it into sub-VU.
The process is based on an homogenous criterion computed on some specific features describing the spectral or structural information of the image. As a VU has a semantic signification and a small size it’s often not divisible using its raster view if there is no external phenomenon which interact with the unit. Nevertheless, if the unit has been altered it’s often composed of a main sub-VU representing the most important part of the VU, one (or at most two) sub-VU having a semantic definition (in the case of ecological units, they are linked to landslide, deforestation, etc.) and a reject sub-unit composed of non identified or useless area (such as shadow).
The localisation and identification of the sub-VU are done in two main ways. Firstly in a supervised way, where both localisation and identification are done at the same time by a semantic and numeric definition of each sub-VU. We use in this case a classifier where each class is a sub-VU. Secondly in a semi-supervised or unsupervised way by applying a clustering method where at most the number of clusters is set. In this way, the identification must be done in a second step.
We will now present the details of this Raster-Vector cooperation. Starting from a raw image (Fig. 8-a), and a vector splitting of the space (Fig. 8-b), we fuse both information (Fig. 8-c). The image is an extract of a very high resolution IKONOS satellite image (1m resolution) allowing an accurate classification (Gougeon, 2001) of vegetation (Junying, 2005), (Yu, 2006), (Johansen, 2007).
We compute an homogeneity coefficient on each Raster Unit (RU) (Fig. 8-d) in order to localise and later split the non uniform ones. The homogeneity coefficient is based on the measure of the compactness of the feature vectors computed on each pixel of the RU. This vector is composed of mean and standard deviation of each Color band (Angwine, 1998) and textures features (over each color band too): co-occurrence matrices (Gotlieb, 1991), Gabor filters (Manjunath, 2010), Laws filters (Laws, 1980), Hue moments (Hu, 1962) and fractal dimension (Mandelbrot, 1977). There is a total of 25 normalized features including geometrical, statistical, frequential and fractal description of the RU (Abadi, 2008), (Scarpa, 2006).
Then the decision to split or not the VU depends on an empirical threshold of the homogeneity coefficient. In this case, the threshold has been set to 0.6 leading to one non homogenous unit (Fig. 8-d) with a coefficient of 0.37.
Non uniform VU localisation
Now we will localize and extract the sub-VU by analyzing the spectral and structural information contained in the RU. In this case, a semi-supervised classification is applied on the non homogenous RU extraction (Fig. 9 a). We use a K-Nearest-Neighbour (KNN) algorithm and set the number of classes to 3, with the aim to obtain classes corresponding to shadow, main ecosystem and secondary ecosystem. The Fig. 9-b) shows the classification results after a filtering post-treatment to eliminate isolated pixels. The class repartition is as follow: class 1 in blue (81%), class 2 in red (13,8%) and class 3 in yellow (5,2%).
The interesting sub-RU is identified has being the class 3 and corresponding to the a deforestation (Fig. 9-c). The identification has been made manually taking into account expert knowledge and environmental information. For example, this sub-VU could be a landslide or a deforestation, the choice has been made according to the slope of the Unit. Then a sub-VU is produced (Fig. 9-d).
Vector Unit splitting using raster information
The use of raster information to both evaluate the homogenity of the VU and to split the VU into sub-VU is an efficient way to produce new vector information. The predivision of the image with the VU combined with the homogeneity evaluation considerably reduce the complexity of the classification problem by replacing the classification of the whole image by a selection of reduced classifications applied to non homogeneous RU.
The main problem of this process is the threshold the user has to fixe to decide to split or not a RU. A common way to use this tool and which avoids fixing the threshold is to sort the units according to their homogenity value and to let the operator take the decision. Indeed, as a post identification of the sub-VU must be done by an operator, the best way is to let him appreciate the relevance of a sub division.
This chapter presents in a first part a vector model for fuzzy data sets within a GIS and in a second part a raster and vector cooperation to divide the space into elementary units represented in a vector way and called Vector Units (VU). The fuzzy vector model offers a new way to represent data which have diffuse borders without the limitations of classical approach which are based on a rasterisation of the information or on a regular evolution of the transitions in each direction. This model tries to combine both the flexibility of the vector representation and the accuracy of the raster representation. This fuzzy model allows revealing full transition classes that a strict model doesn’t allow. The raster-vector cooperation is linked to the mixing of both raster features (colour or textures) and vector features (humidity, elevation, etc.).
In a second part, this chapter deals with the use of raster features to split VU into sub-VU. The raster features are used to evaluate the homogeneity of the VU and in case of insufficient homogeneity to localise the region of interest in order to produce new sub-VU.
These two raster-vector cooperations are ways to produce flexible and useful information. They have been successfully used to classify the forest of a Caribbean Island named Guadeloupe and to localise landslide within its Natural parc.
Some improvement could be done concerning the model and particularly concerning the thresholds to define the different fuzzy belts. But the possibility to store the membership degree vectors in each VU allows producing as much fuzzy models as required.
Concerning the use of raster information to subdivide VU, a comparison (or a combination) with object oriented classification could be done (Maillot, 2008), (Forestier, 2008), (Hudelot, 2008). Object oriented classifications directly start with the raw images divided into elementary raster units (RU) according to a segmentation process (such as Watershed). The main drawback of this approach is the lack of semantic of the RU and the difficulty to retrieve semantically defined objects.
Persuasive technology (PT) can be defined as “design, research, and analysis of interactive computing products created to change people’s attitudes or behaviors” [1]. As technology can be used as a promoter of sustainable behavior, many studies have investigated the possibilities to persuade people within the context of environmental sustainability (e.g., increase consumers’ awareness of energy consumption [2]. However, most of these studies have shortcomings that limit their long-term effectiveness. Although behavioral models (e.g., Transtheoretical Model of behavior change [3], the Goal-setting Theory [4], the Fogg Behavior Model [5]) are very useful for conceptualizing the impact of persuasive technology, most of them cannot easily be applied to the design or assessment of persuasive systems directly because they do not provide appropriate methodological support [6, 7]. For example, through a user experience assessment of existing persuasive software applications, Condori-Fernandez et al. [7] found that some relevant non-functional requirements had not been addressed, and consequently users experienced negatively in using such kinds of systems.
As the identification and management of non-functional requirements in software projects are challenging [8], various assessment models have been proposed for software product quality (e.g., ISO/IEC 25010 quality model). In the software engineering community, a software sustainability model consists of both sustainability-related requirements and quality requirements (e.g., [9, 10, 11]). Lago et al. [12] defined software sustainability based on a four-dimensional model that adds the technical dimension to the social, environmental and economic dimensions that already appear in the Brundtland report [13]. Condori-Fernandez and Lago [9] proposed a Sustainability-Quality (SQ) model for supporting the identification of quality requirements that contribute to the four-dimensional model of software-intensive systems1. The multidimensional approach of Becker et al. [15] adds the individual dimension to the four sustainability dimensions [12]. However, Calero et al. [10] define sustainability only in terms of energy consumption, resource optimization and perdurability, and they do not consider the individual, social, and economic dimensions.
According to Assefa and Frostell [16], for a system to be deemed socially sustainable, it should at minimum enjoy wider social acceptance. In this respect, ensuring the quality of User Experience (UX) is important for increasing the likelihood of accepting socially software systems (e.g., [17]).
In order to provide support for discovering non-functional requirements (NFR) that contribute to sustainability dimensions, we present a Negative User Experience (NUX)-based approach for deriving sustainability-quality requirements, with special emphasis on the social and technical dimensions. Then, the approach is applied in existing software applications designed for preventing RSI.
The following sections provide a detailed account of our work. Section 2 describes the SQ model and PSD model on which the NFR discovery approach is based. Section 3 presents the NUX based approach for deriving sustainability requirements. As a result of applying the approach, we present first the design of a user study. And the discovered nonfunctional requirements and features are reported in Section 5. Finally, we draw the conclusions.
In this work, we adopted the PSD model [18] as the theoretical framework for our research, and the SQ model [9].
The PSD model [18] is a recent conceptualization for designing, developing and evaluating persuasive systems. It consists of (a) the premises behind any persuasive system, (b) the persuasion context and (c) the persuasive software system features. Hence, according to the PSD model any persuasive system is based on eight premises detailed in [18] and listed here: P1: Useful; P2 User-friendly; P3: Unobtrusiveness; P4: Open for transparency; P5: Cognitive Consistency; P6: Incremental; P7: Information technology partiality; P8: Direct and indirect routes to persuasion.
The analysis of the
The PSD model describes persuasive software system features grouped in four categories: (i) The
According to the PSD model, a behavior change can be divided into three categories:
C-Change - or compliance change, is to make sure that the user complies with the request of the behavior change support system.
B-Change - or behavior change, is to elicit a more enduring change than simply compliance a couple of times. Short time behavior change is easier to achieve than long-term behavior change.
A-Change - or attitude change, is to influence the users’ attitudes rather than behavior only. Changing the attitude of a user may be the most difficult type of change to achieve by a behavior change support system.
The outcomes of these C, B and A-Changes are the formation, alteration or reinforcement:
F-Outcome - It means the formulation of a pattern in a situation where it did not exist before.
A-Outcome - It means a change in the response of a user to an issue.
R-Outcome – It means the reinforcement of current attitudes
The SQ model [9] is defined in terms of four
Each dimension is characterized by a set of
Our SQ model supports the identification of sustainability design concerns, and the quality assessment of software architecture. The list of measurable attributes of the SQ model and corresponding contributions to the four dimensions can be found at [9], which has been empirically evaluated [19, 20].
In this section, we present the process needed for deriving sustainability-quality requirements from NUX results. As shown in Figure 1, the process consists of three stages: (i) UX assessment for understanding user needs, (ii) translating user needs into NFRs, and (iii) deriving sustainability-quality requirements from identified features and NFRs. The first two stages correspond to the NFR discovery approach proposed in [21], which uses the PSD model as a means to identify non-functional requirements (NFR) for a persuasive software system.
Deriving sustainability quality requirements from NUX results.
UX assessment is supported by a wide range of research methods available, ranging from attitudinal evaluations (e.g. UX questionnaire, think-aloud) to behavioral evaluations (e.g. eye-tracking, activity trackers, emotions measurement). In this phase, in contrast to Sonnleitner et al. [22], we focus on negative User Experience (NUX) that is caused by the lack of fulfillment of needs during the interaction with a software product (a RSI software in our case). The effect of NUX impacts on the user attitudes (“what people say”) and user behaviors (“what people do”). The outcome of this stage is the user feedback, information used for understanding user needs interacting with any persuasive software application.
In this stage, the user feedback from the UX assessment is used through two mapping steps to obtain a first group of NFRs and a set of categories of features respectively:
In order to facilitate the identification of quality requirements and features that contribute to the sustainability of software systems, a graph database has been created using the Neo4j Bloom tool. Such as shown in Figure 2, the data scheme includes elements from the SQ model (i.e., Dimension, Characteristic, Attribute) and the PSD model (i.e., feature and category), as well as the corresponding relationships among these elements. The relationships among the different elements (nodes) are represented by colored edges.
Scheme with types of nodes and relationships in the graph database.
For instance, the edge COMPOSED_OF (green) is used for representing that a quality characteristic is defined in terms of a set of quality attributes of the SQ model. Similarly, there is a composition relationship between the features and categories of the PSD model (briefly introduced in Section 2.1).
Having the graph database, designers will be able to run (predefined) queries in the Neo4j browser. Query results are rendered either as a visual graph or a table format. Figure 3 shows an example of a query result that displays all the quality attributes related to the usability characteristic and their corresponding contribution to the sustainability dimensions.
Example of a query result in Neo4j: Quality attributes related to usability and their contributions to sustainability dimensions.
For illustrating the application of our approach for deriving sustainability quality requirements from a UX assessment, in the following section we present the design of a user study that aims to assess the experience of existing persuasive software applications for preventing RSI.
We targeted office employees working with computers (i.e. desktop, laptop) as RSI typically arises among this kind of user. A total of 30 people from four universities working in offices were invited to participate in the study. Twelve participants accepted our invitation. 3 were female and 9 male, whose age ranged from 21 to 45 years old.
Table 1 shows the software available in the market, characterized by several commonplace functions, although their implementation can differ between systems:
Break reminder (BR) – Remind to take breaks based on several factors like elapsed time, how much/intensely you are working, natural rest patterns, times of day, etc.
Tracking (TK) – Track information like break-taking patterns, working hours per day, repetitions (e.g., keystrokes, mouse clicks).
Biofeedback (B) – Gain greater awareness of body functions primarily using instruments that provide information on the activity of those same systems.
Training (TN) – Provide information on topics including workstation setup, body positioning, work-efficiency tips, psycho-social information, etc.
Name | Features | Operative system | |||
---|---|---|---|---|---|
Break reminder | Tracking | BioFeedback | Training | ||
Stretch, 2013 | X | X | X | Windows | |
Workrave, 2013 | X | X | X | Windows | |
Pokoy, 2014 | X | Linux | |||
Quick Pause, 2014 | X | X | Windows | ||
AntiRSI, 2016 | X | Mac OS | |||
SmartBreak, 2016 | X | X | X | Windows, Mac OS | |
EyeLeo, 2016 | X | X | Windows |
Common functionalities of RSI software.
For practical reasons and given that participants self-reported to work with either Windows 10 Pro operating system or MAC OS, they were offered the two most complete options available, Workrave or SmartBreak, so that the study can run in their natural working environment. Although both applications, Workrave and SmartBreak, deliver reminders and enforce to take breaks, there are some differences as explained next.
Training coach demonstrating exercises to stretch body muscles in Workrave.
According to the PSD model, two types of behavior changes are addressed by Workrave and SmartBreak, as shown in Table 2.
B-change | A-change | |
---|---|---|
F-Outcome | Workrave helps people to form healthier habits by doing stretching exercises | |
A-Outcome | SmartBreak helps people to get awareness on their stress level determined based on the way how keyboard and mouse is used. |
Outcome/changes analysis for Workrave and SmartBreak.
The study was conducted in the natural settings of the subjects for 1 week (5 working days). Hence, they used their own computer and had to install by themselves the software application of their choice. The 8 subjects with a Windows computer installed Workrave, whereas 4 users who had a laptop with Mac OS installed SmartBreak. Only one participant had used an RSI software in the past (i.e. Workrave).
Participants were given the following instruments:
An UX questionnaire that is based on the User Needs Questionnaire (UNeeQ) [22]. It is composed of two parts described as follows:
Open questions to capture in free form text any additional issue regarding user experiences and feelings, either positive or negative, as well as to allow participants to make suggestions for improvements to the software applications.
Demographic questions included age, gender, weekly working hours, and more specific questions that allowed us to know if our respondents suffered RSI in the past (i.e. identifying symptoms and possible triggers).
Premise | Items | Day1 | Day5 | |
---|---|---|---|---|
P1 | … I was doing something good for my body and mind. | I1 | X | X |
P2 | … joy and pleasure. | I2 | X | X |
P4 | … safe from uncertainties. | I3 | X | |
P1, P6 | … that I was more physically active (e.g. stretching exercises). | I4 | X | |
P1, P6 | … that I was less mentally tired | I5 | X | |
P3, P2 | … disturbed by the RSI software | I6 | X | |
P4, P6 | … that I was developing a deeper understanding on how to prevent RSI | I7 | X | X |
P5 | … that I was acting according to my true interest (e.g. moving naturally to well-being) | I8 | X | |
P5 | … that the system was supporting me to make my commitments (e.g. changing work habits) | I9 | X | |
P6 | … a better sense of physical well-being. | I10 | X |
Adapted user needs questionnaire for RSI software.
Each participant was asked to install one of the selected RSI software (Workrave or Smartbreak) in his/her own computer, and configure the timing parameters according to the given instructions.
Then, we asked them to use the RSI software while they were working with their computer for 1 week.
During the study, participants were allowed to adjust the values of any parameter (e.g. break times, sound of alarm) whenever they considered necessary. As the study was conducted in their natural working environment, they were allowed to abandon and not to finish the study. They were informed beforehand about the length of the study and the existence of a final questionnaire that should only be filled in if the study rules were met regarding duration and working with the computer normally.
Given UX can change over time, the data collection was carried out at two moments: (i) At the end of the first day, participants were asked to complete a first UX questionnaire. As some items of the UX questionnaire could not be experienced immediately by users, we considered only a subset of items for the first round listed in Table 3. (ii) At the end of the study, participants were asked to complete the second UX questionnaire.
In this section we present the results obtained following the NUX-based discovery process introduced in Section 3.
According to our demographic data collected at the beginning of the study, we found that most of our participants worked more than 40 hours per week. The distribution is as follows: 5 subjects reported to work more than 45 hours per week. 5 subjects worked between 40 and 45 working hours. Only 2 subjects worked between 38 and 40 weekly hours.
Regarding RSI symptoms, most of the participants indicated felt fatigue, aching or shooting pain. 3 subjects did not experience none of the symptoms shown in Table 4. One of these 3 subjects decided to drop-out from the study after experiencing with the RSI software for 1 day.
Nine of our subjects considered stress as the main trigger of their RSI symptoms, followed by a bad ergonomic posture (8 subjects). Surprisingly, we found that omitting breaks and maximum exposure to technology were considered by less than half of the subjects that experienced RSI symptoms.
At the beginning of the study we found that the RSI software was not enjoyable enough and pleasant. The most positive answers were given by 3 participants using Workrave, who indicated have enjoyed -more or less- with the software. However, despite this non-positive feeling, 7 of out 11 participants felt that using an RSI software was somewhat useful (“more or less” and “significantly high”) to support their own wellness (body and mind). Over half of the participants reported that they understood how to prevent RSI during their first interaction with the software. Figure 5 shows the frequency distribution of the responses in detail regarding these three items that concern about enjoyment, usefulness, and awareness respectively.
User experience after day 1 (N = 11).
Quality attributes of the SQ model defined according to [
A total of 9 participants completed the study and filled in the final questionnaire after day 5. Table 5 shows the answers’ frequency to the user experience questionnaire items and the weighted average. After several days of use, it is confirmed that the extent of “enjoyment and pleasant” is low. The “safe from uncertainties” score reinforces the idea that participants were not fully sure on how the break reminder and monitoring features work (e.g. how the breaks based on the stress level is determined by SmartBreak).
RSI symptoms | Responses |
---|---|
Aching or shooting pain. | 50% (6) |
Fatigue or lack of strength. | 50% (6) |
Weakness in the hands or forearms. | 25% (3) |
Tremors, clumsiness and numbness. | 17% (2) |
Chronically cold hands, particularly the fingertips. | 17% (2) |
Difficulty with normal activities like opening doors, turning on a tap. | 8% (1) |
RSI symptoms experienced by participants.
Items | 0 | 1 | 2 | 3 | 4 | W-Avg |
---|---|---|---|---|---|---|
Joy and pleasure. | 4 | 3 | 2 | 0 | 0 | 0.77 |
a better sense of physical well-being. | 3 | 3 | 2 | 1 | 0 | 1.11 |
I was doing something good for my body and mind. | 0 | 3 | 3 | 3 | 0 | 2 |
Safe from uncertainties. | 6 | 0 | 2 | 1 | 0 | 0.77 |
I was more physically active (e.g. stretching exercises). | 2 | 4 | 1 | 2 | 0 | 1.33 |
I was less mentally tired | 4 | 3 | 1 | 1 | 0 | 0.89 |
Disturbed by the RSI software | 0 | 4 | 2 | 1 | 2 | 2.11 |
I was developing a deeper understanding on how to prevent RSI | 0 | 3 | 3 | 3 | 0 | 2 |
I was acting according to my true interest (e.g. moving naturally to well-being) | 1 | 3 | 2 | 3 | 0 | 1.78 |
The system was supporting me to make my commitments (e.g. changing work habits) | 2 | 3 | 2 | 2 | 0 | 1.44 |
UX based on the needs fulfillment on day 5 (N = 9).
Most of the participants perceived that the software provided “a bit” support for items related to the physical and mental well-being. This feeling was confirmed by the last two items, where participants did not show to have the impression that the software was supporting their commitments and supporting behavior change in a natural way; therefore, more feedback from the software could be needed along with a different strategy to communicate how the software works and help to attain goals. Overall, it seemed that participants have got a moderate belief of “doing something good for their body and mind”, and have been developing some moderate understanding on how to prevent RSI.
In order to understand the possible variations of the user experience reported after the first day, we carried out an individual analysis with the participants (S1, S2, S5, S6, S9, S11, S12) who finished the study.
What stands out in Table 6 is that there was not any variation in enjoyment and pleasant from day 1 to day 5. Similarly occur for the other two items concerning usefulness and awareness. However, what we can remark is that there were two few positive variations (colored in green), which correspond to subjects S2 and S9. Both subjects started rating negatively, but at the end of the study, they considered that the software was good for their own wellness, and to understand better on RSI prevention. Both continued considering their level of enjoyment as negative though.
Items | Day 1 | Day5 | |
---|---|---|---|
joy and pleasure. | 4 | ||
3 | |||
2 | S6 | ||
1 | S2, S12 | ||
0 | S5, S9, S11 | S5, S9, S11 | |
I was doing something good for my body and mind. | 4 | ||
3 | |||
2 | S5, S12 | ||
1 | S5, S11 | ||
0 | |||
I was developing a deeper understanding on how to prevent RSI | 4 | ||
3 | S6 | ||
2 | S1, | S1 | |
1 | S5, S11, | ||
0 | S5, S11 |
User experience variations along the study.
This was mainly because breaks used to be considered as interruptions. (S2)
We observed also a negative variation (colored in red) that was detected in subject S1, and subject S12. (S1)
Participants rated the four items of in second part of the UX questionnaire that correspond to positive/negative feelings and experience (i.e. “I had a positive experience”, “I had a positive feeling”, “I had a negative experience”, “I had a negative feeling”). The collected responses at day 1 and day 5 (see
Highly | More or less | Not at all | Not at all | More or less | Highly | |||||
---|---|---|---|---|---|---|---|---|---|---|
Workrave | 9% | 18% | 9% | 5% | 32% | 23% | 14% | 18% | 9% | 9% |
Smart Break | 0 | 0 | 9% | 5% | 14% | 9% | 9% | 9% | 0 | 0 |
9% | 18% | 18% | 10% | 46% | 32% | 23% | 27% | 9% | 9% | |
Workrave | 0 | 22% | 17% | 11% | 17% | 11% | 11% | 22% | 22% | 0 |
Smart Break | 0 | 0 | 6% | 28% | 0 | 11% | 0 | 17% | 6% | 0 |
0 | 22% | 22% | 39% | 17% | 22% | 11% | 39% | 28% | 0 |
Frequency distribution for overall positive and negative UX.
Day 1 | Day5 | |
---|---|---|
1.4 | 1.73 | |
2.68 | 2.51 |
Overall UX scores on day 1 and day 5.
In order to calculate the overall UX scores, we averaged the values assigned by the respective participants using Workrave (WR) or SmartBreak (SB) along the study, which are depicted in Table 8. It shows the corresponding overall UX of both RSI software was somewhat negative.
On the overall positive UX, data revealed that software applications did not manage to raise a significant or very positive UX. At the end, the ratings for positive experiences suggest that systems were better considered than they were at the beginning, low to moderate though.
Overall negative UX frequencies suggest that at the beginning there were not much negative UX (notice that 46% replied
In order to discover requirements that should be addressed by a persuasive software application (RSI software in particular) designed for helping people change their behavior to achieve healthier habits, our analysis focuses on the UX results and open-ended descriptive user responses (comments and suggestions).
By mapping user needs (first step of second stage) that were not fulfilled and what people would like to have (results presented in Section 5.1), we found that the premises P1 (Useful), P2 (user friendly), P3 (Unobtrusiveness), P4(Open), P5 (Cognitive consistency), P6(Incremental) were affected, which were accordingly translated into usefulness, pleasure, unobtrusiveness, transparency, cognitive consistency, and awareness requirements. For instance, from the questionnaire (I4, I5) some participants showed their perceptions that the RSI software was not so useful for keeping them more physically active and less mentally tired. It is also important to remark that the usefulness perception of a software system could vary along the time. (i.e. at the beginning some users expected the system would help to change their habits positively but at the end this did not happen).
Analyzing these unfulfilled user needs, we found that both RSI software apps lack some features related to dialog support, primary activity support and credibility categories (second step of second stage). For example, Table 9 shows the discovered requirements like transparency, awareness, and consistency, which are helpful for implementing features from the
Category of features | UNeeQ Item | User comments (C) and suggestions (S) | Premises | NFR |
---|---|---|---|---|
Dialog Support | I7, I10 | C: “I cannot really say I may be satisfied with RSI prevention as I do not know about the state of my condition.” S: “Give supporting messages that create awareness on RSI (why it is important)...” | P4, P6 | |
I8, I9 | C: “the RSI software made me realize that I am frequently taking brakes even without tool support” | P5 | ||
Primary activity support | I2 | C: “I was always annoyed by it, especially when it freezes the screen while I am in the middle of some work.” C: “the RSI software resulted to be quite annoying when a break was triggered” C: “The animation starts the exercise but the instructions are in a long hard to read text, so at the beginning it is annoying to read and see how the animation started and you cannot follow it as still reading” | P2 | Usability (operability) |
I1, I4, I5 | C: “It reminds me that I must relax”. | P1, P6 | Effectiveness | |
I6 | S: “..somehow having a do not disturb mode in which it does not interrupt you but uses a more subtly easy to inform you” C: “Interruptions while typing and (interruptions) of the reasoning flow while working.” | P3, P2 | ||
I6 | C: “I was busy sending some important emails, and I had to wait for a couple of minutes for the computer to unlock.” C: “I tried to follow the instructions of the break, but sometimes is not possible for me because you cannot interrupt your activity of you are completing something urgent or you are discussing our taking with someone else using the screen...” | P3 | Timeliness | |
S: “It would also nice to be able to choose your set of exercises, or changing them for time to time.” S: “blocking interruption stressed me. So, I preferred the non-blocking interruption” | P6, P8 | Tailorability | ||
C: “Suggested exercises are not clear and I had a small period of time to understand them” | P8 | Learnability | ||
C: “The RSI software resulted to be quite annoying when a brake was triggered during a Skype call.” C: “The software actually did not do anything. I did not get any alert, any break, etc. (I was following a course on coursera, which meant mostly watching a video) so maybe that is the reason.” | Adaptability | |||
Credibility | I3 | C: “The system showed me a stress bar when I did not feel to be stressed” | Trust | |
C: “I better understood the meaning of the countdown in the breaks (of the mouse is touched then it is stopped...). I had the feeling that when I skipped breaks, there were appearing often, and therefore interrupting my working activity is not nice.” | P4 |
NFR found from the UX assessment.
Considering the relations of the SQ model for each NFR(attribute) identified in the previous stage, we found out other relevant requirements that contribute to the sustainability dimensions.
For example, regarding the
It similarly occurred for usefulness, a key quality attribute that contributes to the economic, social and technical dimensions (see
Given preferences can vary significantly over users with different profiles (ages, interests, etc.), tailorability is an important requirement that must also be considered. From user suggestions, we found that this requirement was not fully addressed. We also found that the break reminder could have a negative effect on the user’s experience due to that this reminder sometimes occurred in a not favorable time (lack of timeliness).
Finally, the third category,
Table 9 presents the 6 NFRs discovered at the second stage (text in bold in the last column) and 7 NFR derived from the SQ model at the third stage. By means of our approach, in this stage we can also determine (i) the new potential features that should be considered in further versions of the software for RSI prevention, and the sustainability dimensions that can be covered with the implementation of the discovered NFRs. Some of the identified features as potential improvements are shown in Table 10. For example, the self-monitoring feature might help to address the usefulness of applications because the system would be able to provide other means to track user status through self-monitoring.
NFR | Sustainability Dimension | Candidate Features |
---|---|---|
Usefulness | Economic, Social and Technical | Self-monitoring (HELPS_TO-->) |
Unobtrusiveness | Social | Tunneling and Reduction (<−-HELPS_TO) |
Timeliness | Social | Rehearsal and Tunneling (<−-HELPS_TO) |
Tailorability | Social and Technical | Tailoring and Personalization (<−-HELPS_TO) |
Transparency | Social | Suggestion, Real-world feel, Praise, Verifiability, Authority, Reminders, Third-party endorsements, Trustworthiness, Rewards, Expertise, Surface credibility (<−-HELPS_TO) |
Example of candidate features and sustainability dimension covered by discovered NFR.
At the beginning | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
Workrave | 3 | 1 | 2 | 1 | 1 |
Smart Break | 1 | 1 | 1 | 0 | 0 |
Workrave | 2 | 2 | 2 | 1 | 1 |
Smart Break | 1 | 1 | 1 | 0 | 0 |
Workrave | 3 | 1 | 1 | 2 | 1 |
Smart Break | 2 | 0 | 1 | 0 | 0 |
Workrave | 4 | 0 | 1 | 2 | 1 |
Smart Break | 1 | 1 | 1 | 0 | 0 |
Overall UX at the beginning of the study (N = 11). Data collected from the RSI case presented in [7].
At the end | 0 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
Workrave | 1 | 2 | 1 | 2 | 0 |
Smart Break | 1 | 0 | 1 | 1 | 0 |
Workrave | 1 | 0 | 3 | 2 | 0 |
Smart Break | 1 | 0 | 2 | 0 | 0 |
Workrave | 2 | 1 | 1 | 2 | 0 |
Smart Break | 0 | 3 | 0 | 0 | 0 |
Workrave | 1 | 1 | 2 | 2 | 0 |
Smart Break | 0 | 2 | 1 | 0 | 0 |
Overall UX at the end of the study (N = 9). Data collected from the RSI case presented in [7].
In this section, we discuss the threats to the validity of our user study.
A threat related to the
Considering our user study took five working days and it was conducted in a real setting with a null control, the threat of having a high number of dropouts (
The first threat to consider is concerned with the
The second threat is concerned with the
The present research presents an approach for deriving sustainability quality requirements from negative user experience. It starts by understanding the fulfillment of user needs through a UX assessment (first stage). We used the PSD model as the theoretical framework for designing our empirical research of persuasive systems. A user study with 12 subjects working on their natural office environment was carried out. Our UX assessment focused on two popular software systems for preventing RSI (i.e. Workrave and Smartbreak). This study revealed that generally most of the subjects had a negative UX with both RSI software at the end of the study. It is important to remark that from our UX variation analysis, UX of the participants was not much negative at the beginning (46% replied “Not at all”), but along the study their experiences and feelings were changing to be more negative.
From the UX results based on needs fulfillment and user comments/suggestions, 6 NFRs of RSI software (i.e., usefulness, pleasure, unobtrusiveness, transparency, cognitive consistency, and awareness) were discovered (second stage). Then 7 additional NFRs were derived from the SQ model by means of the existing predefined relations among quality attributes that contribute to the social, technical, economic, and environmental dimensions (third stage). All these requirements are helpful for identifying candidate features related to dialog support, primary activity support and credibility categories. Addressing requirements such as awareness, pleasure and consistency, RSI software apps will be in a better capability to provide relevant, motivating and adequate feedback to users. The second group of requirements such as unobtrusive, timely, learnability, usability (operability), usefulness, adaptability will enable RSI software apps to provide a better activity support for the system goal achievement (i.e. reduction and prevention of RSI, main goal of our selected software). The third group of requirements relates to the perceived credibility of the software system, where transparency and trust are very important requirements that positively could affect users to continue using the software system.
In order to support the derivation of requirements and features for improving the sustainability of persuasive systems (RSI software apps in our case), our SQ model along with its relationships with the PSD model has been implemented in Neo4j Graph platform, allowing us to search for potentially relevant attributes by querying and navigating the model interactively.
Our journals are currently in their launching issue. They will be applied to all relevant indexes as soon as they are eligible. These include (but are not limited to): Web of Science, Scopus, PubMed, MEDLINE, Database of Open Access Journals (DOAJ), Google Scholar and Inspec.
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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. 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