Advantages and disadvantages of existing sensor technologies.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
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Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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As enabling technology, real-time human activity monitoring plays an important role in many human-centric applications in different areas such as healthcare, security, surveillance, smart building, etc., particularly to protect elder people and children from some bad incidents. Due to the advanced development in medical, science and technology, the human average lifespan has increased rapidly where people are getting healthier and having longer lives, thus increased the aging population worldwide. The United States’ medical research agency, known as National Institute of Health (NIH), reported that in 2012, 8.0% (or 562 million) of the 7 billion global human population are aged 65 and over, and the percentage has increased by 0.5% (or 55 million) in 2015 [1]. Based on the aging trends, NIH has projected that by 2050, the older population will grow substantially up to 17% (or 1.6 billion) throughout the world [1]. However, most of elderly people spend their extra lifespan in unhealthy manner, with often debilitating illness and disability due to the deterioration of physical or mental functions caused by age-related diseases. In fact, the increase in the older population has a slight impact on the increase in disability rates of world’s population [2].
With the increasing of older and disabled population in most countries and regions across the world, human and activity monitoring has gained substantial attention from the research community for ambient assisted living or elderly care application. As reported in [3], majority of the elderly people are more comfortable to live independently at their own home and community. Nevertheless, in the modern society, the conventional ways of taking care of elders in the family are no longer effective. As a result, there is higher demand in the society for the assistive technologies such as an intelligent monitoring system that can record the elders’ daily activities in which family can respectfully monitor their loved ones who live alone at home. Due to the lower income earner after retirement and higher standards of living, many of the elders cannot afford to pay their healthcare cost as well as the expensive healthcare system or private nursing home care services. Nonetheless, various human monitoring technologies have been developed that help elderly people to age in place.
Traditionally, the human activity monitoring technology is a vision-based [4], which requires the use of video camera to monitor the human activity. Although this vision-based is an effective security measure approach as it can retains the records with high resolution, it also has several drawbacks that involves cost-inefficiency for large-scale deployments, energy consumption, and serious user privacy concerns if it is used in inappropriate places such as lavatory or bathroom, bedroom, and even nursing room. However, in several applications like elderly care and assisted living, monitoring human activities in these privacy areas is very crucial and necessary. For instance, lavatory or bathroom is one of the potential places for falling due to its slippery condition, thus activity monitoring in this place is very important for elderly fall detection system in detecting the falling event [5]. Meanwhile, the activity monitoring in the bedroom is very important for patient sleep monitoring system in detecting unusual sleeping behavior. In fact, the video camera requires a good lighting area ineffective in the dark and has limited view angles.
In recent years, thousands of research study on human activity monitoring has been conducted involving the replacement of traditional vision-based approach with various technologies such as acoustic-based [6, 7], motion-based [8, 9], body-worn sensors [10, 11], gyroscope [12], as well as smartphone [13, 14]. While such approaches address the privacy concern issue, they are sensor-based or in other words impose the requirement that special sensors, that is to be attached to, carried or worn by the subject for an effective activity monitoring. This is inconvenient and inappropriate for human usage especially the elders or people with brain-related diseases (Alzheimer, amnesia, dementia, etc.) to remember each day to wear or to activate those sensors. Furthermore, the whole monitoring process is ineffective and futile if the subject forgets to carry the sensor. Besides, the acoustic-based approach is range limited and prone to false detections since it can only be used in a short range and can easily be influenced by other audio signals [15]. The motion-based sensor such as a single accelerometer is not able to provide sufficient information to the system if used alone, hence need to combine with other sensors for more efficient activity monitoring [16]. Nevertheless, both vision- and sensor-based approaches bear with huge costs due to expensive equipment, installation, and maintenance. All the advantages and disadvantages of above approaches are summarized in Table 1.
Category | Sensor technology | (+) Advantages (−) Disadvantages |
---|---|---|
Vision-based | Video camera | + Effective security measure + Maintain records – Interfere with privacy – Ineffective in the dark – High computational cost |
Motion-based | Accelerometer Gyroscopes PIR | + No privacy issue + Lower cost (PIR) + High detection accuracy – Raise physical discomfort issue (accelerometer and gyroscopes) – No direct linear or angular position information – Low range and line-of-sight restriction (PIR) – Prone to false detection – Insensitive to very slow motions |
Sound-based | Ultrasonic Acoustic Audio (microphone) | + Very sensitive to motion + Objects and distances are typically determined precisely + Inexpensive (audio) – Work only directionally (ultrasonic) – Sensitive to temperature and angle of the target (ultrasonic) – Easily be influenced by other audio signals/noise – Prone to false detections – Range limited |
Sensor-based | Body-worn sensors (Body sensor networks) | + High detection accuracy + No privacy issue – Expensive devices (sensors) – Disturb or limit the activities of the users – Required sensors installation and calibration |
Advantages and disadvantages of existing sensor technologies.
Recently, Radio Frequency (RF)-based approaches have received significant research attentions to be employed in the human presence detection and activity monitoring based on different wireless radio technologies such as RFID [17, 18], Wi-Fi [19, 20], ZigBee [21, 22], FM radio [23, 24], microwave [25], etc. According to studies on the impact of human presence and activity on the RF signal strength [26, 27, 28], it has been proven that the existence and movement of human body in wireless radio network environment will interfere the wireless signal profiles, either in constructive or destructive manners, in which will change the RF communication pattern between the wireless transceivers. This phenomenon is called radio irregularity, which often consider as a drawback in RF communication. In RF-based human detection and activity monitoring, researchers have seen the radio irregularity phenomenon as a benefit in which it can be exploited as sensing tools to locate the human presence in the indoor environment and discriminate human activities or gestures. Since RF-based human activity monitoring approaches only exploit the wireless communication features, there is no need for expensive physical sensing equipment and modules, which accordingly reduce the cost, ease the deployment, reduce the energy consumption, and protect user privacy [29].
The RF-based approaches can be classified into device-bound and device-free. Like the sensor-based, the device-bound RF-based approach requires the on-body wireless sensors or devices (such as RFID tags or cards, Bluetooth wristbands, smart watches, etc.) to be attached to the subject, which has been known as one of the drawbacks. Hence, the subject is required to actively participate in the activity recognition and monitoring process by always remembering to activate and carry the wearable wireless devices. This device-bound system is also known as active monitoring system and the subjects are usually willing to be monitored by the system. Therefore, we refer the subject in this active monitoring system as an active target. As an example, daily activities, such as walking, sitting, lying, falling, etc., of an active target wearing a simple RFID tag can be tracked using RFID readers [30, 31]. Another example is that an active target carrying mobile phone or other Wi-Fi-embedded devices can be easily tracked by Wi-Fi detectors or monitor [32, 33].
Although the on-body wireless sensors such as RFID tags and RFID cards are commercially available and relatively low cost compared to other wireless technologies, their placement on the target’s body may cause physical discomfort [34], especially the elders under long-term monitoring. Recent research works introduce the placement of RFID in the environments and objects instead of target’s body for activity monitoring [35, 36]. However, reading multiple RFID tags at once may cause malfunction due to signal collision, thus anticollision algorithms are required in which incur an extra cost [37]. On the contrary, device-free RF-based approach, known as device-free localization (DFL), is a passive monitoring system that can locate and monitor human position and activity without the subject’s participation, where the subjects do not need to carry or wear any radio devices. They are usually unaware with the system’s existence, and possibly want to avoid being monitored [21]. The subject in this passive monitoring case is referred as passive target.
In this chapter, we review the recent progress of DFL for indoor environment prioritizing on human activity monitoring with a particular focus on the monitoring systems targeting personal health and assisted living applications. Our aims are to provide a comprehensive review on the topic and to quickly update the researchers beyond this field the state of art, potential, opportunities, challenges, opens issues, and future directions of activity recognition using DFL technology. To the best of our knowledge, although there exist surveys on human activity monitoring and recognition using vision-based [4, 38], wearable sensors [10, 39, 40], mobile phones [41, 42], there are only few surveys published in this research field on human activity monitoring using device-free RF-based [29, 43, 44, 45], including a general architecture of existing work especially in the context of healthcare and assisted living applications. Surveys as in Refs. [46, 47] are specifically on the Wi-Fi-based approaches. However, we do not focus on the classification approaches of human activity, as there exist several in-depth literatures on human activity classification methods [48, 49, 50, 51].
The organization of the chapter is as follows: Section 2 “RF-based DFL Technology” briefly discusses the concept of DFL in the perspective of human activity monitoring as understood within this study and provide an extensive review on the existing works. We decompose the taxonomy of the existing RF-based DFL technologies for human activity monitoring into measurement-based categories, regardless the type of wireless radio technologies used. Section 3 “Opportunities and Potential” presents the potential applications based on the state-of-art of RF-based DFL technology. Based hereon, in the last Section 4 “Challenges, Open Issues and Future Directions,” we outline the challenges and the possible solutions, discuss the open issues, and comment on the possible future research direction of activity recognition using DFL technology.
Historically, the DFL analogy was firstly introduced by Youssef et al. in 2009 as device-free passive (DfP) for location determination, in which the subject is not equipped with a radio device, or not required to actively participate in the localization system [52]. The concept of DFL relies on the fact that any changes on the radio network environment will fluctuate the received signal profiles, i.e., due to reflection, diffraction, absorption, or scattering phenomena. DFL exploits the potential of ubiquitous deployed Internet of Things (IoT) [53] devices for indoor localization by leveraging the RF fluctuations as an indicator of presence of obstruction, i.e., object or human body. In [54], we have briefly defined the concept of DFL technology in the context of human detection and counting, together with the comprehensive review on the publications related to DFL research.
In correspondence to the tremendous progresses on DFL research, Scholz et al. have expanded the area of DFL technology in the context of activity recognition by introducing the concept of device-free RF-based for human activity monitoring as device-free radio-based activity recognition (DFAR) [55]. Instead of utilizing radio signal analysis for object detection and tracking, it can also be utilized in the DFL technology to recognize specific human movement, and even their activities and gesture. For instance, the fluctuations of ambient and local continuous signals have been exploited in detecting human daily activities such as walking, lying, crawling, or standing [56]. To ease the reader’s understanding, we defined the DFL and DFAM systems as:
We illustrate the overall conceptual framework of RF-based DFL technology for human activity monitoring as in Figure 1, including the three important modules: wireless radio sensor network (WRSN), human detection (HD), and human monitoring (HM). The WRSN is a self-configured wireless network consisting of radio devices connected wirelessly, acting as the sensors, for monitoring and recording of the physical or environmental conditions, and organizing the collected information to a predefined central location for processing. The WRSN module works by detecting the availability of radio-embedded devices (sensors) for human presence detection, localization, and activity monitoring, as well as the deployment of the radio sensor networks. WRSN can be deployed in the real-world environments using any radio devices that utilize the similar technology or IEEE standard. For instance, Wi-Fi-based sensor network can be deployed using any devices that utilized the IEEE802.11 wireless local area network (WLAN), while ZigBee-based sensor network can be deployed using devices that utilized the IEEE802.15.4 wireless personal area network (WPAN).
The overall conceptual framework of the RF-based DFL system.
The sensors in WRSN collect the information of current environment and forward the information to be processed by the HD module. HD module consists of detection and localization algorithms which analyze the information and automatically discover the presence of target, the number of target, the location of the target, the body temperature of the target, the activities performed by the target, the humidity of the environment, etc. HM module consists of activity classification algorithms connected with the designated context aware-based activity reasoning engines depending on the applications. Once an activity is detected, the HM module will observe, retrieve, and recognize the activities and alert the designated context aware-based activity reasoning engine to interpret ongoing events successfully or initiate actions as needed. For example, a ZigBee-based sensor network is deployed in a single bedroom apartment for elderly care application as depicted in Figure 2.
ZigBee-based sensor network deployed for elderly care application with the integration of mobile apps visualization.
Following on the DFAM research in [55], many research works on human motion detection and activity monitoring have been presented utilizing different radio technologies such as RFID [35, 36, 37], WiFi [5, 19, 20], ZigBee [21, 22], FM radio [23, 24], microwave [25], etc., adopting different signal descriptors such as Receive Signal Strength (RSS) [57, 58, 59, 60, 61], Channel State Information (CSI) [5, 20, 62, 63, 64], Doppler effect [25, 65], and Packet Received Rate (PRR) [66], without neglecting the easy-of-use problem and physical discomfort issue. In the following subsection, we decompose the taxonomy of the existing RF-based DFL technologies for human activity monitoring into signal descriptor categories such as RSS-based, CSI-based, amplitude-based, Doppler-based, and PRR-based, regardless the type of wireless radio technologies used.
Similar to human presence detection, activity monitoring using RSSI-based DFL technology exploits the RF-signal fluctuation features, in which the components of the received signal are blocked, absorbed, and reflected by the human while performing an activity, inducing the RF signal in the vicinity of receivers into a specific characteristic pattern. Such pattern can be identified and classified for the corresponding activity by exploiting the changes on the RSS of the affected wireless links.
In [57], Sigg et al. introduced three types of RF-based DFAR systems: active continuous signal-based, active RSSI-based, and passive continuous signal-based DFAR; which exploit the fluctuation of RSS due to human movement and activities. Both active and passive continuous signal-based proposed are USRP Software Defined Radio (SDR)-based system, which are deployed using specialized SDR devices. Meanwhile the RSSI-based DFAR system utilized the 2.4 GHz INGA sensor nodes [57]. The performance accuracy of the proposed DFAR systems is then compared with the performance accuracy of the motion-based recognition system. In the motion-based recognition system, accelerometers are attached to the subjects while performing the activities. By implementing three well-known classifier algorithms that are Naive Bayes, Classification Tree, and k-nearest neighbor (k-NN), their proposed RF-based DFARs are able to achieve comparable results with the motion-based system. Furthermore, they evaluated the performance of the proposed RF-based DFAR system in the presence of multiple subjects performing different activities and the impact of increasing the number of receiving devices. However, the proposed systems required specialized SDR devices, where the hardware availability remains as an open issue [60].
Sigg et al. expanded their work by designing an RSS-based activity recognition system for the mobile phones [58, 59] based on the advantages of mobile phones as personal devices that often carried everywhere. The proposed system utilized the Wi-Fi-RSSI values of incoming packets at a mobile phone for the activities classification. Unlike other body-worn devices, the function of mobile phone in an RSS-based activity monitoring system remains feasible even when it is not carried by the user. By default, the firmware and operating system (OS) of a standard mobile phone do not provide privilege for user to access its hardware as well as desired RSSI information. Thus, work in [58] utilized a modified firmware, which allows mobile phone to run Wi-Fi interface in monitor mode and developed tools to process RSSI sample captured on mobile phone in monitoring simple human activities such as walking and phone handling. Meanwhile, work in [59] focused on recognizing 10 different single-handed gestures utilizing the same modified firmware and tools developed in [58] with average accuracy of 0.51 when distinguishing all gestures and is able to achieve average accuracy of 0.60 and 0.72 when reducing to 7 and 4 gestures, respectively. Unfortunately, the OS root access incompatibility, complicated firmware modifications, and low accuracy are the major issues in the real-world applications.
The proposed RF-based DFAR systems in [57, 58, 59] utilized the RSS features as per listed in Table 2 and several combinations of those features for the activities classification. Assume that a wireless network environment consists of a static transmitter node or access point (AP), and a static receiver node or monitoring point (MP). Let
Feature | Description | Definition |
---|---|---|
Mean | Represents the static changes in RSS Provides means to distinguish a presence of static person as well as the exact location | |
Variance | Represents the volatility of RSS Provides the estimation on changes in nearby receivers such as movement of person | |
Standard deviation (SD) | Can be used instead of the variance The interpretation of SD and variance is identical | |
Median | Represents static changes in RSS. More robust to noise than the mean Let the ordered set of samples | |
Normalized spectral energy | Represents a measure in the frequency domain of the RSS Can be used to capture periodic patterns such as walking, running, or cycling | |
Minimum and maximum | Both represent extremal signal peaks Can be used to estimate movement and any changes in environment | |
Signal peaks within 10% of a maximum | Reflections of the obstructed signal strength at a receive antenna Peaks of similar magnitude indicate that movement is farther away Can be used to indicate near-far relations and activity of individuals | |
Mean difference between subsequent maxima | Similar magnitude of maximum peaks within a sample window indicates low activity in an environment or static activities The opposite will indicate dynamic activities |
Since works in [58, 59] focused more on hand gestures, Gu et al. [60] proposed an online Wi-Fi RSSI fingerprint-based DFAM concentrated on human activity, which has a flexible architecture and can be integrated in any existing indoor WLANs, regardless the environment conditions. Based on the preliminary results of the human activities impact on the Wi-Fi characteristic study [60], the Wi-Fi RSSI fingerprint can be extracted and exploited to distinguish different activities since each activity has their own RSSI fluctuation patterns. To reduce the difficulties in distinguishing activities having similar RSSI footprints, such as sitting and standing, the proposed system adopted a novel fusion classification tree-based algorithm. The system has been evaluated through extensive real-world experiments based on six main activities (that are sleeping, sitting, standing, walking, falling, and running) and achieved average accuracy of 72.47% for all activities, thus outperforms Naive Bayes, Bagging, and k-NN classifiers.
Monitoring human activity using RFID technology is often associated with the physical discomfort issues as user needs to wear or carry the RFID devices. However, there exist several studies that implemented the RFID technology in the different way for the device-free activity monitoring [61, 67]. Instead, the RFID devices are attached to the walls, furniture, and daily objects. This approach is known as passive RFID-based DFL. Thanks to the rapid advancement and sophistication in cheap sensing and wireless technology for introducing various RF-embedded devices with an open-source platform such as TelosB [68], IRIS [69], Waspmote [70], etc., that can operate in real-time environment based on the “plug and sense” concept where information like RSS can easily be captured. However, RSS measurements suffer from high uncertainties since the signal profiles tend to fluctuate depending on the environment, thus unpredictably experience interference, complex multipath propagation, and being noise-sensitive. In addition, RSS-based system experiences accuracy and coverage limitation due to the lack of the frequency diversity. Thus, RSS-based approach is only suitable for coarse-grained human activity monitoring.
Most of the research on Wi-Fi-based DFL utilized the CSI, one of the Wi-Fi features extracted from the physical layer of radio wireless system, for indoor location estimation and human motion and activities monitoring due to its stability and robustness in complex environment compared to RSSI. CSI information are available in commercial wireless devices such as network interface controller (NIC), which is also known as network interface card, network adapter, LAN adapter, or physical network interface. Unlike RSSI value which is usually measured from one packet, CSI value is measured per orthogonal frequency-division multiplexing (OFDM) from each packet and uses the frequency diversity technique to reflect the multipath propagation signals caused by human motion and activity, thus making it suitable for monitoring the fine-grained signals of human activities and motions.
Based on [19, 63], consider a Wi-Fi-based DFL system with NICs continuously measure the CSI variations in every received Wi-Fi frame of multiple wireless channels. Let
where
The CFR values consist of S metrices of CSI measurement with dimension of
Since the radio signals travel from a transmitter to a receiver through multiple paths depending on the surrounding, the measured
where
Figure 3 shows the scenario where Wi-Fi signals transmitted from an AP (Tx) to an MP (Rx) are traveled through different paths, which are the line-of-sight (LoS) path and paths reflected by wall and human body. Let the path of reflected signal due to human body is the
Multipaths scenario experienced by the Wi-Fi signals caused by human movement.
where
The phase of each path can be precisely measured only if the transmitter is in synchronization with the receiver. Unfortunately, due to hardware limitation and environment variations, the CFO of the commercial Wi-Fi devices, denoted as ∆f in Eq. (3), cannot be ignored. The impact of CFOs of devices running on IEEE 802.11n standard causes random variation in the phase of CSI, which allows devices to continuously transmit Wi-Fi frames based on frame aggregation mechanism, thus creating a phase interference scenario. It is difficult to precisely measure even the small phase shift in
To ignore the phase interference introduced by CFO, Wang et al. [63] introduced a CSI-speed model into their activity recognition and monitoring system (CARM), which considers the relationship of CFR power variations instead of CFR phase variation to the human movement speeds. Since the CSI streams of human movements are correlated, it is hard to extract the real trend of CSI caused by the human movement for feature classification purpose. Therefore, works in [5, 19, 63] applied the principal component analysis (PCA) to discover the principal component of the CSI fluctuation pattern caused by human activity motion to be used as features for activity classification. In [5], Li et al. analyzed five features from CSI principal component which are normalized standard deviation (STD), median absolute deviation (MAD), interquartile range (IR), signal entropy, and duration of human motion to recognize seven different human daily activities. By applying random forest-based classification algorithm, work in [5] verified the validity of their proposed human monitoring system in both the LoS and Non-LOS (NLoS) scenarios as 95.43% and 91.4%, respectively. Meanwhile, activity monitoring system based on hidden Markov model (HMM) classifier algorithm proposed by Wang et al. [19, 63] achieved an average recognition accuracy of 96%.
Although undesired noise from the environment may disturb some of the streams, since CSI is measured using OFDM method, other streams which are not affected by the noise still can provide the real trend of CSI information. Since CSI contains more information than RSSI, it is suitable for fine-grained activity monitoring. However, unlike RSSI which is available in almost all wireless devices, CSI only can be obtained from devices with specific NIC cards such as Intel 5300 and Atheros 9390 [19].
When wave such as ultrasonic and radio wave is transmitted to moving target, the wavelength of the reflected wave shifts depending on the direction and velocity of the movement. This is known as Doppler effect or Doppler shift. Recently, the principle of the Doppler effect has been proposed by researches in device-free radio sensor network for human activity monitoring and data gathering of real-world environment [25, 65] since the Doppler-based technology has the ability to accurately detect movement and eliminate the stationary noise of the environment [66]. The same principle of Doppler effect is applied to a Doppler sensor, having a beat signal as an output, in which frequency is defined as the difference between transmitted and received waves. Due to its high detection accuracy, work in [25] has deployed a 24-GHz microwave-Doppler sensor for a device-free activity monitoring system to recognize the daily activity of three passive targets with an average recognition rate of 90.6% based on eight different activities.
Based on the Doppler possibility study in [25], assume that a radio wave source at a fixed position transmits a radio wave with frequency
where
Let the signal of the transmitted wave
and
From Eq. (8), the received signal depends on the object size and its distance from the source. The beat signal
From Eq. (9), the amplitude and frequency of the Doppler shift are highly correlated with the range of the object and its motion speed. Thus, any human movement and activities with different speeds will have different Doppler shifts. Those human activities can be estimated and analyzed by extracting the features of Doppler signature in the frequency and time domains.
Work in [65] proposed an in-home Wi-Fi signal-based activity recognition framework for e-healthcare applications utilizing the passive micro-Doppler (m-D) signature classification. A fast Fourier transform (FFT) was used on the cross-correlation product of the baseline and monitored signals to find the exact delay
where
Although the constant false alarm rate (CFAR) detection is not suitable for the indoor environment due to the ambiguity peaks and direct signal interference (DSI) problems [65], DSI is an important feature in Doppler-based as it can be used to distinguish different signatures. Instead, a weighted standard deviation is proposed as the indicator to detect the m-D signature without eliminating the ambiguity peaks and DSI. PCA can be applied to reduce the dimension of dataset and eliminate the undesired noise. Finally, the Doppler signature is classified using a sparse representation classifier (SRC) with subspace pursuit (SP) technique, which outperforms the well-known support vector machine (SVM) in terms of classification accuracy and coverage. The sparsity level in SRC can easily be controlled and adjusted, thus making the proposed activity recognition framework a feasible tool, which is very suitable for the real-time healthcare applications, especially for the new users since it is not required to re-training the system.
It has been proved that RF signal features extracted from RSS and CSI information discussed in Sections 2.1 and 2.2 can be used to distinguish the type of movement as well as recognize the activities performed. However, RSS is sensitive to the shadowing effect and experiences the complex multipath propagation behavior, which makes it only suitable for monitoring coarse-grained activity. Meanwhile, CSI, which provides powerful information suitable for fine-grained activity monitoring, faces hardware issues since the information is only available from NIC embedded devices.
In [66], Huang and Dai presented a novel PRR-based DFL system for human movement recognition under the NLoS scenario based on packet state characteristic from link state information (LSI). The LSI, which contains more physical information such as RSSI, packet delivery rate, packet state, packet delay, packet loss, time arrival, and time interval of the received packet, etc., can be accessed from the network layer. Human movement in the radio network environment will block or reflect the signal and cause significant changes on the signal propagation path. This results in the fluctuation of channel link quality as well as slow fading effect.
By exploring the LSI features such as packet state and packet arrival time, different activities performed by a person in the monitoring area can be identified. Work in [66] exploited the PRR measurement to identify the link state. Assume the
Consider a wireless network environment in a hallway consists of a transmitter Tx and a receiver Rx as shown in Figure 4. When a person is moving into the hallway area, there will be four possible trajectories: walking from Tx to Rx, walking from Rx to Tx, walking from Tx to Tx, and from Rx to Rx. When the person moves into the hallway area, the link state quality tends to fluctuate in terms of the PRR. Different moving trajectories in the hallway will generate different fluctuation patterns of PRR with respect to the person position in hallway, thus the direction of walking can be identified. The distance of moving traces with different trajectories can be calculated using the Euclidean distance equation as in (12) and the walking direction of the traces can be identified using the K nearest neighbors (KNN) algorithm. The Euclidean distance between the PRR of the testing trace
Node deployment in the hallway area.
Since PRR cannot be used to distinguish the speed of the movement, other link state information known as the received packet arrival time is used to measure the speed. However, the time interval of received packets is highly correlated with the moving speed. Therefore, several parameters, such as autocorrelation function
DFL for human and activity monitoring is the promising technology for collecting data about the human presence and activity patterns. The technology is much cheaper than the existing traditional monitoring system using video camera. It consists of radio nodes comprising the appropriate sensor array along with computational devices that transmit and receive data wirelessly, and capable of providing information on an unprecedented temporal and spatial scale. The DFL system is an easy-to-install motion tracking system developed based on the IoT to improve the quality of life as well as provide intelligence and comforts to the user especially the disabled. Users, especially family, can respectfully monitor their loved ones who live alone at home, without requiring them to wear devices or change their habits. The system can be integrated with mobile and web apps which allow user to easily monitor their home/office from anywhere, in real time. The system can be made to replace the existing RFID monitoring system which always raises physical discomfort and is less reliable since more than one tag can respond at the same time.
In recent years, there has been an increase in the number of patient admission in hospitals worldwide, whether federal or nonfederal, especially in the developed countries due to the increase of older and disabled population [71, 72]. In England, for instance, the older population (aged 65–69) has grown by 34% in 2016 after a decade from 2.2 million in 2006, together with the series of increasing hospital admissions by 57% from 0.8 million, over the same period of time [71]. This causes most hospitals to experience inadequate bed problem to admit patients, thus slowing down the work of medical staff, especially at the casualty department or emergency department (ED). Patients started to complain about the slow services, which lead to bad reputation of the hospital. By implementing DFL system, federal and nonfederal bodies can introduce remote home healthcare services where patients can be monitored and advised from anywhere. These services help patients to improve their function and live with greater independence. Using this system, existing patients are taught to manage their wellness level, and safely manage their medication regimens; meanwhile, medical staff can remotely monitor and estimate the health condition of patients by interpreting the patients’ daily routines. In this situation, patients will remain at home, avoiding hospitalization or admission to long-term care institutions. If the daily routine of a patient is abnormal as expected such as too long sleeping or resting in bed, the patient might be sick and should be visited soon for closer examination.
Recent advances in medicine allow people to live longer and healthier compared to the previous generations, which lead to an increase in the number of elder people. Aging brings many challenges to them due to cognitive decline, chronic age-related diseases, as well as limitations in physical activity, vision, and hearing. With an increase in age-related diseases, there will also be a rise in individuals unable to live independently. However, due to the higher standards of living, children nowadays are too busy working to earn money for living and have no time to care for their parents. This leads to an increase in the number of elderly people in the federal- and nonfederal-owned welfare or nursing home; meanwhile, there will be a shortage of professionals trained or care-giver to work with the aging population. Given the fact that most of the elderly people prefer to stay in the comfort of their own homes, and given the costs of private nursing home care, it is imperative to develop technologies that help elderly people to age in place. By implementing the DFL technology as an ambient assisted living tool, family can respectfully monitor their loved ones who live alone at home, without requiring them to wear devices or change their habits. The DFL system can be integrated with mobile and web apps which allow user to easily monitor their home or office from anywhere, in real time. This advantage makes DFL technology very suitable for monitoring persons’ activities (especially the elderly, disable people, and patient suffering from Alzheimer’s disease) without causing them physical discomfort with the wearable devices or sensors. In addition, it is a challenge for them to remember each day to wear or to activate those devices.
Automatic and monitoring control in “smart” building, i.e., for home or office, was developed based on the IoT and WSN technologies to improve the quality of life as well as provide intelligence and comforts to the user especially the disabled. The DFL technology can be expanded not only for monitoring purposes, but also as an application server that can control and initiate actions as needed. For example, in an office building where few people are working together, the proposed DFL technology can enhance the existing lighting, heating, and air conditioning system by providing information of current environment such as the presence of people, the number of people as well as their location, the body temperature of the occupants, the activity performed by the occupants, the humidity of the environment, etc. If there are too many electronic devices in use or too many occupants in the office which leads to an increased temperature of the room, the building heating system can be adjusted or automatically lowered based on the information provided by the DFL system. If there is no people presence in several areas inside the office especially during lunch break, the lights and air-conditioning in those areas can be automatically switched off. Government as well as private bodies can implement this technology in all their buildings which definitely will reduce the utility cost.
In the recent years, the RF-based DFL approach has made tremendous achievements and becomes a popular research topic in localization and activity monitoring area. In the previous section, we have provided a review of existing human activity monitoring system based on different approaches. However, there are still significant challenges and open issues worth exploring and require further in-depth research. Moreover, the performance of existing systems can be further optimized, improved, and extended. In this section, we present a list of challenges and open issues together with the possible future research directions to be addressed by the researchers.
In this chapter, we provided an extensive review on human activity recognition using RF-based DFL technology, targeting human-centric applications such as healthcare, well-being, and assisted living applications. We provided the details information on concept of DFL and DFAM, together with the feature selection approaches based on different signal descriptors and the potential applications. We presented an extensive review on the existing and on-going works qualitatively and discussed on the challenges, limitations, and future research directions relevant to this field. We believe that this DFL technology has great potential in the future, which can benefit humans and will be one of the key areas of research that worth to be explored.
Ketamine was first synthesised in 1962 and put into clinical practice in 1970.It has a chiral structure and consists of two optical isomers S (+) and R (−) forms. Ketamine is commonly used for anaesthesia in the paediatric population. A recent survey identified standard induction agents used in children varied from Etomidate in 26.9% (7/26), propofol in 19.2% (5/26), a combination of benzodiazepines and ketamine in 19.2% (5/26), and barbiturates in 11.5% (3/26) [1]. The use of anaesthesia in paediatric age group outside the OR includes dental offices, endoscopy suites, cardiac catheterization laboratory, radiology facilities, radiation oncology departments, paediatric intensive care units (PICUs), and emergency departments. Patients aged less than 3 years routinely require anaesthesia prior to any procedure. By 7 years of age however most children can tolerate non-painful exams and treatments without anaesthesia support [2, 3]. In the OR Ketamine may be used for sedating the child prior to inducing GA in order to decrease anxiety due to parental separation. However the psychological side effects of Ketamine as well as availability of other agents made Ketamine less popular as an induction agent. Induction technique preferred in children is usually inhalational route especially with the availability of Sevoflurane.
The American Society of Anaesthesiology (ASA) defines four levels of sedation (Table 1): minimal (anxiolysis), moderate (conscious), deep (purposeful response to vigorous stimulation), and general anaesthesia (unresponsive). A variety of pharmacologic agents are available to sedate and anaesthetise patients. Conscious sedation can be defined as, “A controlled state of depressed consciousness that allows the protective reflexes to be maintained, retaining the patient’s ability to maintain a patent airway independently and continuously and allows appropriate response by the patient to physical stimulation or verbal command.” A patient can progress from one level to another during sedation given in various doses. Hence continuous monitoring and vigilance is of utmost importance. The drugs used must be titrated to achieve the desired effect, prevent overdose and sudden loss of consciousness. Prior to even short procedures requiring sedation, the child must be evaluated thoroughly –check for any comorbidities like seizure history, previous surgeries, allergic reactions, birth history, developmental milestones attained etc. Airway should be examined to anticipate any difficult airway-enlarged tonsils, congenital defects etc. The blood investigations necessary should be ordered as per need just like prior to a child for major surgical procedure. Adequate fasting guidelines should be explained and ensured. An understanding of the pharmacodynamics and pharmacokinetic effects of sedating drugs which are going to be used is essential. Appropriate sized airway equipment, venous access, appropriate intraoperative monitoring equipment, properly equipped staff in recovery area and proper discharge criteria should also be checked. Sedation drugs can be administered through various routes—oral, nasal, intramuscular, intravenous (IV), subcutaneous, and inhalational routes.
Mild | Moderate | Deep sedation | General anaesthesia | |
---|---|---|---|---|
Response to verbal stimulus | Normal | Only responds purposefully | Response seen only on repeated painful stimulation | No response even to painful stimulus |
Airway | Not affected | Usually able to maintain airway without intervention | May not be able to maintain airway reflexes | Airway adjuncts like supraglottic airway device or endotracheal intubation required |
Spontaneous Ventilation | Maintains spontaneous respiration | Adequate | May be inadequate | Frequently inadequate |
Cardiovascular Function | No cardiovascular depression | Usually normal | Usually normal | Cardiovascular depression may occur |
ASA levels of sedation.
For conscious sedation drugs are used in sub anaesthetic doses and titrated to obtain adequate effect. Various drugs have been used for conscious sedation in paediatric age group which includes Ketamine. The doses of drugs used for Conscious sedation is given in Table 2.
Drug | Route of administration |
---|---|
Midazolam | IV/Intranasal |
Ketamine | IV/IM/rectal/oral/intranasal |
Dexmedetomidine | IV |
Propofol | IV |
Ketofol | IV |
Opioids (Fentanyl/Remifentanyl) | IV |
Drugs used for conscious sedation.
Ketamine is a phencyclidine derivative which acts as an N-methyl-D-aspartate (NMDA) receptor antagonist at the dorsal horn of the spinal cord [2, 4]. It induces dissociative amnesia and analgesia [5]. Ketamine has the advantage of various routes of administration available for use. Administration routes include intravenous (1–2 mg/kg), intramuscular (2–10 mg/kg), oral (3–6 mg/kg), intranasal (2–4 mg/kg), and rectal (5–10 mg/kg) (Refer Table 3) [6]. Ketamine has many advantages over other drugs especially due to its relative cardiovascular steadiness and restricted effect on the respiratory mechanics. Recovery occurs within 30–120 min, and this allows the patient to be discharged on the same day as the procedure. It has a dose dependent cardiovascular stimulant effect. In children with congenital heart disease, it causes only minor increases in heart rate and mean pulmonary artery pressure during cardiac catheterization procedures [1]. It has various effects on the other systems in the body some of which are listed in Table 4.
Route | Dose |
---|---|
IV | 1–2 mg/kg |
IM | 2–10 mg/kg |
Oral | 3–6 mg/kg |
Intranasal | 2–4 mg/kg |
Sedation | 0.2–0.75 mg/kg IV or 2–4 mg/kg IM |
Dosages of ketamine.
Organ system | Effect |
---|---|
Cardiovascular | Increases heart rate, blood pressure, cardiac output |
Respiratory | Increases the oral secretions, bronchodilator, maintains the airway reflexes |
Neurologic | Dissociative anaesthesia Increase in intracranial pressure, excitatory effects on thalamus and limbic systems, increase in intraocular pressure, increase in cerebral metabolism, increase in cerebral oxygen consumption Emergence delirium |
Effects of ketamine on various systems.
Adverse reactions associated with ketamine include dreams, hallucinations, delirium, agitation, vomiting, increased salivation, and laryngospasm [7]. It causes increase in intraocular and intracranial pressures after its administration. Hence it is not used in patients with glaucoma, open globe injuries, or elevated intracranial pressure [5]. Clinically, ketamine is frequently used to facilitate short, painful procedures in the emergency department [4, 8]. Sedation can be achieved with minimal respiratory depression. However when higher doses are used, one can easily induce general anaesthesia [5].
Ketamine causes hyper salivation and thus needs to be administered with an antisialagogue like Atropine or glycopyrrolate. To prevent hallucinations and delirium it is often combined with short acting benzodiazepines like midazolam.
The combination of ketamine and propofol, known as ketofol is also a popular drug used for procedural sedation. The two drugs when combined act synergistically and thus helps to decrease the dose of each drug independently. The side effects of ketamine which includes vomiting, laryngospasm, and emergence delirium, can be decreased by adding propofol. In the same way using ketamine along with propofol decreases the risk of propofol-induced respiratory depression and hypotension. The combination also provides for analgesia [9]. There is no standard combination mentioned but usually Ketamine and Propofol are mixed in a 1:1 ratio (mg) [10]. According to a prospective randomised controlled study involving paediatric patients undergoing cardiac catheterization, using a propofol: ketamine combination in the ratio of 10:2 (mg) preserved mean arterial pressure without affecting recovery time [11]. Studies which have compared ketofol with propofol have shown that ketofol produces consistent depth of sedation. Patient satisfaction scores were also found to be similar. Propofol causes pain on injection but the combination of propofol with Ketamine reduces pain on injection. The risk of airway and respiratory complications were similar in both groups [12, 13, 14, 15]. Ketofol decreases the requirements of both opioids and propofol. Ketofol is thus an acceptable choice for short procedures in the emergency department or critical care setting [10]. The efficacy, safety, pharmacokinetics, and pharmacodynamics require further evaluation with additional prospective trials in the paediatric population.
With currently available IV anaesthetic agents such as Propofol, barbiturates, opioids etc. which are used frequently in combination with Ketamine for procedures done outside the OR, the complication rates has declined from 23% [16] seen in the 1980s to 1–2%. This is somewhat similar to the complication rates in the ORs [17, 18, 19]. A current study by Owusu-Agyemang et al. [3] showed that use of propofol either alone or in combination with Dexmedetomidine and Fentanyl lowered complication rates to 0.05%.Some newer drugs like Fospropofol have been approved by FDA for sedation purposes. Some drugs like Remimazolam and other Etomidate derivatives are still in clinical trial stages. Some centres have seen the resurgence of inhalational anaesthetic nitrous oxide.
Cancer pain management, especially in terminal stages, can be challenging. Cancer pain is mediated through various pathways, including visceral, nociceptive, neuropathic and central. Currently used agents have limited role in addressing each component and have significant adverse events. The safety profile of Ketamine has been evaluated in a number of trials. The WHO ladder for pain management includes acetaminophen, non-steroidal anti-inflammatory drugs, weak opioids like tramadol and the strong opioids like morphine for cancer pain management. In addition to this, topical local anaesthetics like lignocaine can also be used. However US FDA approval for many of these medications is lacking for use in the paediatric age group.
Safety and efficacy as an anaesthetic and analgesic has been well documented; however, ketamine has not yet been approved as an analgesic agent by the US FDA. This may prevent its free use by many for cancer pain management [20, 21, 22, 23]. When Ketamine is used in doses <1 mg/kg it has minimal depressant effects on cardiovascular and respiratory systems as it produces only minimal sedation (Refer Table 1) [20, 24]. However it produces analgesia and modulate central sensitization, hyperalgesia, and opioid tolerance. Hence the National Comprehensive Cancer Network guidelines has recommended considering oral or intravenous (IV) ketamine for pain not responding to other analgesics [20, 25]. Ketamine has been used through various routes of administration-IV, IM, oral, sublingual Intranasal rectal and even epidural in patients with malignancy. The bioavailability of intranasal Ketamine was found to be 45–50% [26, 27].
A review of five studies of ketamine for cancer pain in children showed that patients treated with oral and IV ketamine had only few adverse events reported. However, these studies were all retrospective. Participants’ cancer diagnoses include acute myelogenous leukaemia, myelodysplastic syndrome, osteosarcoma, metastatic giant malignant mesenchymal tumour, glioblastoma multiforme, neuroblastoma, Ewing sarcoma, spindle cell sarcoma, synovial cell sarcoma, and Wilm’s tumour [28]. There are several very small case series or individual case reports of children being treated with ketamine for pain with promising results. For example, at Melbourne, a protocol for IV ketamine administration is being used to treat children who have been unresponsive to two doses of morphine. Additional dose of ketamine (0.1 mg/kg) given as a bolus has helped to achieve effective pain control. These doses have not been associated with hallucinations or dysphoria. However, this report does not enumerate percentages of patients with adequate pain control after treatment with ketamine [29]. A prospective phase I trial of oral ketamine in the dose of 0.25–1 mg/kg given in divided doses in children with chronic noncancer pain has been undertaken [30].
Children with severe cancer pain have been treated with ketamine in doses of 3 mg/kg/day given orally [31] and 0.1–1 mg/kg/h given intravenously. In a retrospective review, 8 of the 11 (73%) children and adolescents had decreased need for opioids and improved pain control [32]. The results of these reports suggest that pain control may be achieved with the use of ketamine in children with cancer pain. These doses were well tolerated by the children between 3 and 17 years of age with cancer pain without nausea, sedation, hallucination, respiratory distress, or psychotomimetic effects.
The common side effects of ketamine include nausea, vomiting, occurrence of bizarre dreams, hallucinations, emergence agitation, seizures. It causes tachycardia and hypertension and thus is contraindicated in patients with cardio vascular illnesses. It also increases in intra ocular pressure and is thus contraindicated in open eye injuries.
Some studies have shown lorazepam given along with Ketamine to decrease the psychotomimetic side effects of ketamine [32]. Ketamine administered through the epidural route in children has shown to produce fewer side effects due to Ketamine. This also decreased the opioid consumption during the procedure [33]. The neurotoxicity caused due to Ketamine appears to be less in children than in adults. There are a few case reports of laryngospasm caused when Ketamine is given intramuscularly or in higher doses [33, 34]. One case report of a ketamine infusion for a child reports mycolonic movements in the child [35]. The report is unclear as to whether this was related to ketamine or the child’s spinal cord tumour. There have been occasional incidences of reversible cystitis with chronic exposure to ketamine [36, 37].
The incidence of respiratory complications has been found to be higher with the use of intramuscular administration of Ketamine as compared to intravenous use. An increased incidence of laryngospasm has been reported especially due to the higher dose of ketamine required for effect as well as delayed absorption of intramuscularly administered drug. The incidence of respiratory adverse events was 2.4% with IM ketamine [34].
A retrospective study evaluated the usefulness of combining intranasal Dexmed (2 mcg/kg) and Ketamine (1 mg/kg) for procedural sedation found it to be useful in 93% of patients. The onset of sedation was 15 min and duration was found to be 62 min. Minor complications like nausea and vomiting only were observed in the study in 0.3% of the patients.
More than 11,000 cases have been reported of its use in children with no fatalities being described in the literature by Green et al. [5] the most frequently cited disadvantage is the emergence phenomenon, seen more commonly in adults where the incidence is 5–50% while in children it has been found to be 0–5%. Ketamine increases the salivary and tracheobronchial mucus gland secretions, and hence needs to be combined with an antisialagogue during GA. Emesis is the one of the most common side effect of ketamine. In a review by Green the incidence of vomiting was found to be 10% and more commonly seen in children undergoing dental procedures. Atropine has been found to decrease the emesis by reducing the salivary secretions. Laryngospasm was reported in 0.4% of cases. Laryngospasm was managed with 100% oxygen and positive pressure ventilation using bag and mask [38].
In his study, Embu has described various techniques for burns contracture release. Some case were done with intermittent doses of Ketamine while patients were spontaneously breathing. Some patients were maintained on inhalational anaesthetic after Ketamine induction-either via face mask or LMA (laryngeal mask airway). After adequate surgical release, the patients were intubated by direct laryngoscopy. No airway complications were reported in the study. However, maintaining anaesthesia with an inhalation agent via facemask was found to be technically difficult owing to the proximity to the sterile surgical field [39].
Agarwal et al. have reported use of tumescent local anaesthesia for the release of neck contracture due to burns in 30 patients. 0.5–1.0 mg/kg of IV ketamine were used in these children at the start of the case. They were maintained on ketamine during the procedure also as intermittent IV boluses (dose has not been specified). No airway complications had been reported. All patients were maintained on spontaneous ventilation throughout the case [40].
Preservative-free ketamine added to caudal bupivacaine has been shown to improve the duration of analgesia, without affecting the analgesic intensity in a study done by Martindale et al. [41]. In a recent survey conducted among paediatric anaesthetists in UK by Sanders 32% had reported using epidural ketamine [42]. It is used in a dose of 0.25–1 mg/kg as an additive to bupivacaine or Ropivacaine.
Children often are given regional anaesthesia for pain management following General anaesthesia (GA) in contrast to adult patients. Hence it is difficult to assess the usefulness of the regional technique except by use of surrogate indicators like tachycardia, hypertension. Perfusion Index is a newer technique to detect effectiveness of regional anaesthetic under GA.
Studies have demonstrated that PI can provide an early and reliable indication of the onset of epidural anaesthesia. Intravascular injection of epinephrine-containing local anaesthetic test dose can also be identified in the adult population [6, 8]. However, caudal blocks in paediatric patients are mostly performed under sedation or general anaesthesia, using ketamine or sevoflurane [9, 10]. Data has shown that ketamine itself can affect PI. Thus it is difficult to predict the onset of caudal block using PI in the paediatric patients who have been sedated using Ketamine. A previous study has shown that intravenous ketamine used in paediatric patients produced a fast and long-lasting decrease in peripheral PI. However the study also showed that caudal block reversed the decrease of PI measured in the toe, caused by ketamine anaesthesia in paediatric population. The PI was found to increase beyond the preinduction level. The study also showed that PI response criterion achieved 100% sensitivity and specificity in detecting the effects of caudal anaesthesia under IV ketamine anaesthesia in paediatric patients. However, neither HR nor MAP criteria were 100% reliable. Furthermore, the changes of PI caused by caudal block under ketamine anaesthesia were much earlier than those of HR and MAP.
Ketamine being a widely used intravenous anaesthetic in paediatric patients, it has been shown to produce an immediate and long-lasting decrease in peripheral PI due to its sympathomimetic effects through its effects on both central and peripheral mechanisms [17, 18]. In this study, a drop in PI was observed within one minute after the injection of ketamine (2.36 ± 0.79 to 1.58 ± 0.61) and after 30 min PI it had decreased to 0.80 ± 0.26, which was far below the baseline value of PI. The changes of MAP lasted about 15 min, and the changes of HR lasted about 5 min following ketamine injection. Caudal block not only reversed the decrease of PI on the toe caused by ketamine anaesthesia in paediatric patients, but also increased PI far beyond the preinduction PI value [43].
The sensory association areas of the cortex, components of the limbic system, and thalamus are directly depressed by ketamine. Consequently, higher central nervous system (CNS) centres are unable to receive or process sensory information and its emotional significance cannot be assessed. The result of ketamine administration is anaesthesia, analgesia, suppression of fear and anxiety, and amnesia, which appear to be ideal for the uncooperative child patient.
Ketamine is commonly used for sedation and analgesia during painful procedures because it maintains the cardiovascular and respiratory systems while providing effective sedation, analgesia, and amnesia. However Ketamine-induced emergence reactions like hallucinations, delusions, nightmares, and agitation are shown to be less in children [44]. Ketamine can be used prior to invasive procedures in the ICU like Lumbar puncture, central line insertions. It can be used in management of children with status asthamaticus.
Ketamine has many advantages due to which it is used for sedation in the paediatric population viz. a relatively short duration of action, multiple routes of administration, preservation of airway reflexes, and sympathomimetic properties including increase heart rates and blood pressure. Sedation can be achieved without much respiratory depression. However ketamine has various adverse effects too. These include hallucinations, emergence delirium, agitation, nausea and vomiting, hyper salivation, and laryngospasm. This can cause distress to both the child and parent. Reports of patients developing random movements of the extremities has been reported which renders this drug less than ideal for procedures where the patient must lie perfectly still like in the MRI suite. Thus, ketamine is used along with other sedative agents to counterbalance the side effects and enhance the beneficial effects for each drug rather than as a sole sedative agent for MRI. Ketamine can prevent the cardiorespiratory depression effect of propofol and prolonged recovery of dexmedetomidine by reducing the dose requirements of each drug when used for sedation in children in MRI suite [44, 45, 46, 47, 48].
Exposure to ketamine and other anaesthetic agents during early stages of postnatal brain development increases central nervous system neuronal apoptosis in animals receiving significantly larger and more prolonged doses than used for procedural sedation [49]. No evidence of neuronal injury after a single ketamine based sedation has been seen in small children but repeated use of ketamine for procedures may have detrimental effects. [50, 51].
Ketamine has been used as an induction agent in children with cyanotic congenital heart conditions like Tetralogy of Fallot. This is due to its effect in increasing the systematic vascular resistance and thus decreasing the incidence of righto left shunt. However it can increase the infundibular spasm. Thus it is combined with opioids or propofol. Another recently described alternative to this is Etomidate combined with Ketamine [52].
Recent studies have explored the use of Ketamine in other situations in adult population as well like prevention of postoperative sore throat, treatment of status epilepticus, alcohol withdrawal syndrome, status asthamaticus etc. There has been an increased usage of Ketamine in the acute pain setting to prevent excessive opioid use but these require further studies in the paediatric population. Thus Ketamine is a very useful drug in the paediatric age group which may be combined with other drugs to alleviate its side effects and achieve anaesthesia as well as analgesia.
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