Window Selection Impact in Human Activity Recognition

Signal segmentation is usually applied in the pre-processing step to make the data analysis easier. Windowing approach is commonly used for signal segmentation. However, it is unclear which type of window should be used to get optimum accuracy in human activity recognition. This study aimed to evaluat e which window type yields the optimum accuracy in human activity recognition. The acceleration data of walking, jogging, and running were collected from 20 young adults. Then, the recognition accuracy of each window types is evaluated and compared to determine the impact of window selection in human movement data. From the evaluation, the overlapping 75% window with 0.1 s length provides the highest accuracy with mean, standard deviation, maximum, minimum, and energy as the features. The result of this study could be used for future researches in relation to human activity recognition.&nbsp

A quantitative comparison of Overlapping and Non-overlapping sliding windows effects for human activity recognition using inertial sensors

The sliding window technique is widely used to segment inertial sensor signals, i.e., accelerometers and gyroscopes, for activity recognition. In this technique, the sensor signals are partitioned into fix-sized time windows which can be of two types: (1) non-overlapping windows, in which time windows do not intersect, and (2) overlapping windows, in which they do. There is a generalized idea about the positive impact of using overlapping sliding windows on the performance of recognition systems in Human Activity Recognition. In this thesis, we analyze the impact of overlapping sliding windows on the performance of Human Activity Recognition systems with different evaluation techniques, namely subject-dependent cross validation and subject-independent cross validation. Our results show that the performance improvements regarding to overlapping windowing reported in the literature seem to be associated with the underlying limitations of subject-dependent cross validation. Furthermore, we do not observe any performance gain from the use of such technique in conjunction with subject-independent cross validation. We conclude that when using subject-independent cross validation, non-overlapping sliding windows reach the same performance as sliding windows. This result has significant implications on the resource usage for training the human activity recognition systems

Central monitoring system for ambient assisted living

Smart homes for aged care enable the elderly to stay in their own homes longer. By means of various types of ambient and wearable sensors information is gathered on people living in smart homes for aged care. This information is then processed to determine the activities of daily living (ADL) and provide vital information to carers. Many examples of smart homes for aged care can be found in literature, however, little or no evidence can be found with respect to interoperability of various sensors and devices along with associated functions. One key element with respect to interoperability is the central monitoring system in a smart home. This thesis analyses and presents key functions and requirements of a central monitoring system. The outcomes of this thesis may benefit developers of smart homes for aged care

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies

Objectives: Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing. Design: Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time). Data sources: Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970–July 2021). Eligibility criteria for selecting studies: Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals. Results: Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as ‘low risk’ or ‘some concerns’. Summary: Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed

Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies.

Objectives Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing. Design Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time). Data sources Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970-July 2021). Eligibility criteria for selecting studies Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals. Results Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as 'low risk' or 'some concerns'. Summary Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed. PROSPERO registration number CRD42021230894

Development of a Wearable Sensor-Based Framework for the Classification and Quantification of High Knee Flexion Exposures in Childcare

Repetitive cyclic and prolonged joint loading in high knee flexion postures has been associated with the progression of degenerative knee joint diseases and knee osteoarthritis (OA). Despite this association, high flexion postures, where the knee angle exceeds 120°, are commonly performed within occupational settings. While work related musculoskeletal disorders have been studied across many occupations, the risk of OA development associated with the adoption of high knee flexion postures in childcare workers has until recently been unexplored; and therefore, occupational childcare has not appeared in any systematic reviews seeking to prove a causal relationship between occupational exposures and the risk of knee OA development. Therefore, the overarching goal of this thesis was to explore the adoption of high flexion postures in childcare settings and to develop a means by which these could be measured using non-laboratory-based technologies. The global objectives of this thesis were to (i) identify the postural demands of occupational childcare as they relate to high flexion exposures at the knee, (ii) apply, extend, and validate sensor to segment alignment algorithms through which lower limb flexion-extension kinematics could be measured in multiple high knee flexion postures using inertial measurement units (IMUs), and (iii) develop a machine learning based classification model capable of identifying each childcare-inspired high knee flexion posture. In-line with these global objectives, four independent studies were conducted.   Study I – Characterization of Postures of High Knee Flexion and Lifting Tasks Associated with Occupational Childcare Background: High knee flexion postures, despite their association with increased incidences of osteoarthritis, are frequently adopted in occupational childcare. High flexion exposure thresholds (based on exposure frequency or cumulative daily exposure) that relate to increased incidences of OA have previously been proposed; yet our understanding of how the specific postural requirements of this childcare compare to these thresholds remains limited. Objectives: This study sought to define and quantify high flexion postures typically adopted in childcare to evaluate any increased likelihood of knee osteoarthritis development. Methods: Video data of eighteen childcare workers caring for infant, toddler, and preschool-aged children over a period of approximately 3.25 hours were obtained for this investigation from a larger cohort study conducted across five daycares in Kingston, Ontario, Canada. Each video was segmented to identify the start and end of potential high knee flexion exposures. Each identified posture was quantified by duration and frequency. An analysis of postural adoption by occupational task was subsequently performed to determine which task(s) might pose the greatest risk for cumulative joint trauma. Results: A total of ten postures involving varying degrees of knee flexion were identified, of which 8 involved high knee flexion. Childcare workers caring for children of all ages were found to adopt high knee flexion postures for durations of 1.45±0.15 hours and frequencies of 128.67±21.45 over the 3.25 hour observation period, exceeding proposed thresholds for incidences of knee osteoarthritis development. Structured activities, playing, and feeding tasks were found to demand the greatest adoption of high flexion postures. Conclusions: Based on the findings of this study, it is likely that childcare workers caring for children of all ages exceed cumulative exposure- and frequency-based thresholds associated with increased incidences of knee OA development within a typical working day. Study II – Evaluating the Robustness of Automatic IMU Calibration for Lower Extremity Motion Analysis in High Knee Flexion Postures Background: While inertial measurement units promise an out- of-the-box, minimally intrusive means of objectively measuring body segment kinematics in any setting, in practice their implementation requires complex calculations in order to align each sensor with the coordinate system of the segment to which they are attached. Objectives: This study sought to apply and extend previously proposed alignment algorithms to align inertial sensors with the segments on which they are attached in order to calculate flexion-extension angles for the ankle, knee, and hip during multiple childcare-inspired postures. Methods: The Seel joint axis algorithm and the Constrained Seel Knee Axis (CSKA) algorithm were implemented for the sensor to segment calibration of acceleration and angular velocity data from IMUs mounted on the lower limbs and pelvis, based on a series of calibration movements about each joint. Further, the Iterative Seel spherical axis (ISSA) extension to this implementation was proposed for the calibration of sensors about the ankle and hip. The performance of these algorithms was validated across fifty participants during ten childcare-inspired movements performed by comparing IMU- and gold standard optical-based flexion-extension angle estimates. Results: Strong correlations between the IMU- and optical-based angle estimates were reported for all joints during each high flexion motion with the exception of a moderate correlation reported for the ankle angle estimate during child chair sitting. Mean RMSE between protocols were found to be 6.61° ± 2.96° for the ankle, 7.55° ± 5.82° for the knee, and 14.64° ± 6.73° for the hip. Conclusions: The estimation of joint kinematics through the IMU-based CSKA and ISSA algorithms presents an effective solution for the sensor to segment calibration of inertial sensors, allowing for the calculation of lower limb flexion-extension kinematics in multiple childcare-inspired high knee flexion postures. Study III – A Multi-Dimensional Dynamic Time Warping Distance-Based Framework for the Recognition of High Knee Flexion Postures in Inertial Sensor Data Background: The interpretation of inertial measures as they relate to occupational exposures is non-trivial. In order to relate the continuously collected data to the activities or postures performed by the sensor wearer, pattern recognition and machine learning based algorithms can be applied. One difficulty in applying these techniques to real-world data lies in the temporal and scale variability of human movements, which must be overcome when seeking to classify data in the time-domain. Objectives: The objective of this study was to develop a sensor-based framework for the detection and measurement of isolated childcare-specific postures (identified in Study I). As a secondary objective, the classification accuracy movements performed under loaded and unloaded conditions were compared in order to assess the sensitivity of the developed model to potential postural variabilities accompanying the presence of a load. Methods: IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (DTW) distance-based Nearest Neighbour algorithm for the identification of twelve childcare inspired postures. Fifty participants performed each posture, when possible, under unloaded and loaded conditions. Angle estimates from thirty-five participants were divided into development and testing data, such that 80% of the trials were segmented into movement templates and the remaining 20% were left as continuous movement sequences. These data were then included in the model building and testing phases while the accuracy of the model was validated based on novel data from fifteen participants. Results: Overall accuracies of 82.3% and 55.6% were reached when classifying postures on testing and validation data respectively. When adjusting for the imbalances between classification groups, mean balanced accuracies increased to 86% and 74.6% for testing and validation data respectively. Sensitivity and specificity values revealed the highest rates of misclassifications occurred between flatfoot squatting, heels-up squatting, and stooping. It was also found that the model was not capable of identifying sequences of walking data based on a single step motion template. No significant differences were found between the classification of loaded and unloaded motion trials. Conclusions: A combination of DTW distances calculated between motion templates and continuous movement sequences of lower limb flexion-extension angles was found to be effective in the identification of isolated postures frequently performed in childcare. The developed model was successful at classifying data from participants both included and precluded from the algorithm building dataset and insensitive to postural variability which might be caused by the presence of a load. Study IV – Evaluating the Feasibility of Applying the Developed Multi-Dimensional Dynamic Time Warping Distance-Based Framework to the Measurement and Recognition of High Knee Flexion Postures in a Simulated Childcare Environment Background: While the simulation of high knee flexion postures in isolation (in Study III) provided a basis for the development of a multi-dimensional Dynamic Time Warping based nearest neighbour algorithm for the identification of childcare-inspired postures, it is unlikely that the postures adopted in childcare settings would be performed in isolation. Objectives: This study sought to explore the feasibility of extending the developed classification algorithm to identify and measure postures frequently adopted when performing childcare specific tasks within a simulated childcare environment. Methods: Lower limb inertial motion data was recorded from twelve participants as they interacted with their child during a series of tasks inspired by those identified in Study I as frequently occurring in childcare settings. In order to reduce the error associated with gyroscopic drift over time, joint angles for each trial were calculated over 60 second increments and concatenated across the duration of each trial. Angle estimates from ten participants were time windowed in order to create the inputs for the development and testing of two model designs wherein: (A) the model development data included all templates generated from Study III as well as continuous motion windows here collected, or (B) only the model development data included only windows of continuous motion data. The division of data into the development and testing datasets for each 5-fold cross-validated classification model was performed in one of two ways wherein the data was divided: (a) through stratified randomized partitioning of windows such that 80% were assigned to model development and the remaining 20% were reserved for testing, or (b) by partitioning all windows from a single trial of a single participant for testing while all remaining windows were assigned to the model development dataset. When the classification of continuously collected windows was tested (using division strategy b), a logic-based correction module was introduced to eliminate any erroneous predictions. Each model design (A and B) was developed and tested using both data division strategies (a and b) and subsequently their performance was evaluated based on the classification of all data windows from the two subjects reserved for validation. Results: Classification accuracies of 42.2% and 42.5% were achieved when classifying the testing data separated through stratified random partitioning (division strategy a) using models that included (model A, 159 classes) or excluded (model B, 149 classes) the templates generated from Study III, respectively. This classification accuracy was found to decrease when classifying a test partition which included all windows of a single trial (division strategy b) to 35.4% when using model A (where templates from Study III were included in the model development dataset); however, this same trial was classified with an accuracy of 80.8% when using model B (whose development dataset included only windows of continuous motion data). This accuracy was however found to be highly dependent on the motions performed in a given trial and logic-based corrections were not found to improve classification accuracies. When validating each model by identifying postures performed by novel subjects, classification accuracies of 24.0% and 26.6% were obtained using development data which included (model A) and excluded (model B) templates from Study III, respectively. Across all novel data, the highest classification accuracies were observed when identifying static postures, which is unsurprising given that windows of these postures were most prevalent in the model development datasets. Conclusions: While classification accuracies above those achievable by chance were achieved, the classification models evaluated in this study were incapable of accurately identifying the postures adopted during simulated childcare tasks to a level that could be considered satisfactory to accurately report on the postures assumed in a childcare environment. The success of the classifier was highly dependent on the number of transitions occurring between postures while in high flexion; therefore, more classifier development data is needed to create templates for these novel transition movements. Given the high variability in postural adoption when caring for and interacting with children, additional movement templates based on continuously collected data would be required for the successful identification of postures in occupational settings. Global Conclusions Childcare workers exceed previously reported thresholds for high knee flexion postures based on cumulative exposure and frequency of adoption associated with increased incidences of knee OA development within a typical working day. Inertial measurement units provide a unique means of objectively measuring postures frequently adopted when caring for children which may ultimately permit the quantification of high knee flexion exposures in childcare settings and further study of the relationship between these postures and the risk of OA development in occupational childcare. While the results of this thesis demonstrate that IMU based measures of lower limb kinematics can be used to identify these postures in isolation, further work is required to expand the classification model and enable the identification of such postures from continuously collected data

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers

Determining the potential of wearable technologies within the disease landscape of sub-Saharan Africa

Thesis (MEng)--Stellenbosch University, 2019.ENGLISH ABSTRACT: Please refer to full text for abstract.AFRIKAANSE OPSOMMING: Raadpleeg asseblief vol teks vir opsomming

Mobile phone technology as an aid to contemporary transport questions in walkability, in the context of developing countries

The emerging global middle class, which is expected to double by 2050 desires more walkable, liveable neighbourhoods, and as distances between work and other amenities increases, cities are becoming less monocentric and becoming more polycentric. African cities could be described as walking cities, based on the number of people that walk to their destinations as opposed to other means of mobility but are often not walkable. Walking is by far the most popular form of transportation in Africa’s rapidly urbanising cities, although it is not often by choice rather a necessity. Facilitating this primary mode, while curbing the growth of less sustainable mobility uses requires special attention for the safety and convenience of walking in view of a Global South context. In this regard, to further promote walking as a sustainable mobility option, there is a need to assess the current state of its supporting infrastructure and begin giving it higher priority, focus and emphasis. Mobile phones have emerged as a useful alternative tool to collect this data and audit the state of walkability in cities. They eliminate the inaccuracies and inefficiencies of human memories because smartphone sensors such as GPS provides information with accuracies within 5m, providing superior accuracy and precision compared to other traditional methods. The data is also spatial in nature, allowing for a range of possible applications and use cases. Traditional inventory approaches in walkability often only revealed the perceived walkability and accessibility for only a subset of journeys. Crowdsourcing the perceived walkability and accessibility of points of interest in African cities could address this, albeit aspects such as ease-of-use and road safety should also be considered. A tool that crowdsources individual pedestrian experiences; availability and state of pedestrian infrastructure and amenities, using state-of-the-art smartphone technology, would over time also result in complete surveys of the walking environment provided such a tool is popular and safe. This research will illustrate how mobile phone applications currently in the market can be improved to offer more functionality that factors in multiple sensory modalities for enhanced visual appeal, ease of use, and aesthetics. The overarching aim of this research is, therefore, to develop the framework for and test a pilot-version mobile phone-based data collection tool that incorporates emerging technologies in collecting data on walkability. This research project will assess the effectiveness of the mobile application and test the technical capabilities of the system to experience how it operates within an existing infrastructure. It will continue to investigate the use of mobile phone technology in the collection of user perceptions of walkability, and the limitations of current transportation-based mobile applications, with the aim of developing an application that is an improvement to current offerings in the market. The prototype application will be tested and later piloted in different locations around the globe. Past studies are primarily focused on the development of transport-based mobile phone applications with basic features and limited functionality. Although limited progress has been made in integrating emerging advanced technologies such as Augmented Reality (AR), Machine Learning (ML), Big Data analytics, amongst others into mobile phone applications; what is missing from these past examples is a comprehensive and structured application in the transportation sphere. In turn, the full research will offer a broader understanding of the iii information gathered from these smart devices, and how that large volume of varied data can be better and more quickly interpreted to discover trends, patterns, and aid in decision making and planning. This research project attempts to fill this gap and also bring new insights, thus promote the research field of transportation data collection audits, with particular emphasis on walkability audits. In this regard, this research seeks to provide insights into how such a tool could be applied in assessing and promoting walkability as a sustainable and equitable mobility option. In order to get policy-makers, analysts, and practitioners in urban transport planning and provision in cities to pay closer attention to making better, more walkable places, appealing to them from an efficiency and business perspective is vital. This crowdsourced data is of great interest to industry practitioners, local governments and research communities as Big Data, and to urban communities and civil society as an input in their advocacy activities. The general findings from the results of this research show clear evidence that transport-based mobile phone applications currently available in the market are increasingly getting outdated and are not keeping up with new and emerging technologies and innovations. It is also evident from the results that mobile smartphones have revolutionised the collection of transport-related information hence the need for new initiatives to help take advantage of this emerging opportunity. The implications of these findings are that more attention needs to be paid to this niche going forward. This research project recommends that more studies, particularly on what technologies and functionalities can realistically be incorporated into mobile phone applications in the near future be done as well as on improving the hardware specifications of mobile phone devices to facilitate and support these emerging technologies whilst keeping the cost of mobile devices as low as possible