Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based PAR domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based PAR system for both personal and impersonal models. To further validate our hypothesis for both personal (The classifier is built using the data only from one specific user) and impersonal (The classifier is built using the data from every user except the one under study) models, we tested single subject validation process for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1

Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services

Smartphones are ubiquitously integrated into our home and work environment and users frequently use them as the portal to cloud-based secure services. Since smartphones can easily be stolen or coopted, the advent of smartwatches provides an intriguing platform legitimate user identification for applications like online banking and many other cloud-based services. However, to access security-critical online services, it is highly desirable to accurately identifying the legitimate user accessing such services and data whether coming from the cloud or any other source. Such identification must be done in an automatic and non-bypassable way. For such applications, this work proposes a two-fold feasibility study; (1) activity recognition and (2) gait-based legitimate user identification based on individual activity. To achieve the above-said goals, the first aim of this work was to propose a semicontrolled environment system which overcomes the limitations of users' age, gender, and smartwatch wearing style. The second aim of this work was to investigate the ambulatory activity performed by any user. Thus, this paper proposes a novel system for implicit and continuous legitimate user identification based on their behavioral characteristics by leveraging the sensors already ubiquitously built into smartwatches. The design system gives legitimate user identification using machine learning techniques and multiple sensory data with 98.68% accuracy

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Toward Dynamic Social-Aware Networking Beyond Fifth Generation

The rise of the intelligent information world presents significant challenges for the telecommunication industry in meeting the service-level requirements of future applications and incorporating societal and behavioral awareness into the Internet of Things (IoT) objects. Social Digital Twins (SDTs), or Digital Twins augmented with social capabilities, have the potential to revolutionize digital transformation and meet the connectivity, computing, and storage needs of IoT devices in dynamic Fifth-Generation (5G) and Beyond Fifth-Generation (B5G) networks. This research focuses on enabling dynamic social-aware B5G networking. The main contributions of this work include(i) the design of a reference architecture for the orchestration of SDTs at the network edge to accelerate the service discovery procedure across the Social Internet of Things (SIoT); (ii) a methodology to evaluate the highly dynamic system performance considering jointly communication and computing resources; (iii) a set of practical conclusions and outcomes helpful in designing future digital twin-enabled B5G networks. Specifically, we propose an orchestration for SDTs and an SIoT-Edge framework aligned with the Multi-access Edge Computing (MEC) architecture ratified by the European Telecommunications Standards Institute (ETSI). We formulate the optimal placement of SDTs as a Quadratic Assignment Problem (QAP) and propose a graph-based approximation scheme considering the different types of IoT devices, their social features, mobility patterns, and the limited computing resources of edge servers. We also study the appropriate intervals for re-optimizing the SDT deployment at the network edge. The results demonstrate that accounting for social features in SDT placement offers considerable improvements in the SIoT browsing procedure. Moreover, recent advancements in wireless communications, edge computing, and intelligent device technologies are expected to promote the growth of SIoT with pervasive sensing and computing capabilities, ensuring seamless connections among SIoT objects. We then offer a performance evaluation methodology for eXtended Reality (XR) services in edge-assisted wireless networks and propose fluid approximations to characterize the XR content evolution. The approach captures the time and space dynamics of the content distribution process during its transient phase, including time-varying loads, which are affected by arrival, transition, and departure processes. We examine the effects of XR user mobility on both communication and computing patterns. The results demonstrate that communication and computing planes are the key barriers to meeting the requirement for real-time transmissions. Furthermore, due to the trend toward immersive, interactive, and contextualized experiences, new use cases affect user mobility patterns and, therefore, system performance.Cotutelle -yhteisväitöskirj

WatchTrace: Design and Evaluation of an At-Your-Side Gesture Paradigm

In this thesis, we present the exploration and evaluation of a gesture interaction paradigm performed with arms at rest at the side of one's body. This gesture stance is informed persisting challenges in mid-air arm gesture interactions in relation to fatigue and social acceptability. The proposed arms-down posture reduces physical effort by minimizing the shoulder torque placed on the user. While this interaction posture has been previously explored, the gesture vocabulary in previous research has been small and limited. The design of this gesture interaction is motivated by the ability to provide rich and expressive input; the user performs gestures by moving the whole arm at the side of the body to create two-dimensional visual traces, as in hand-drawing in a bounded plane parallel to the ground. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we explore the users' mental model for using this interaction by conducting an elicitation study on a set of everyday tasks one would perform on a large display in public to semi-public contexts. The takeaway from this study presents the need for a dynamic and expressive set of gesture vocabulary including ideographic and alphanumeric gesture constructs that can be combined or chained together. We then explore the feasibility of designing such a gesture recognition system using commodity hardware and recognition techniques, dubbed WatchTrace, which supports alphanumeric gestures of up to length three, providing a vibrant, dynamic, and feasible gestural vocabulary. Finally, we explore potential approaches to improve the recognition through the use of adaptive thresholds, n-best lists, and changing reject rates among other conventional techniques in the field of gesture classification

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research