234 research outputs found

    Hybrid Wearable Signal Processing/Learning via Deep Neural Networks

    Get PDF
    Wearable technologies are gaining considerable attention in recent years as a potential post-smartphone platform with several applications of significant engineering importance. Wearable technologies are expected to become more prevalent in a variety of areas, including modern healthcare practices, robotic prosthesis control, Artificial Reality (AR) and Virtual Reality (VR) applications, Human Machine Interface/Interaction (HMI), and remote support for patients and chronically ill patients at home. The emergence of wearable technologies can be attributed to the advancement of flexible electronic materials; the availability of advanced cloud and wireless communication systems, and; the Internet of Things (IoT) coupled with high demand from the tech-savvy population and the elderly population for healthcare management. Wearable devices in the healthcare realm gather various biological signals from the human body, among which Electrocardiogram (ECG), Photoplethysmogram (PPG), and surface Electromyogram (sEMG), are the most widely non-intrusive monitored signals. Utilizing these widely used non-intrusive signals, the primary emphasis of the proposed dissertation is on the development of advanced Machine Learning (ML), in particular Deep Learning (DL), algorithms to increase the accuracy of wearable devices in specific tasks. In this context and in the first part, using ECG and PPG bio-signals, we focus on development of accurate subject-specific solutions for continuous and cuff-less Blood Pressure (BP) monitoring. More precisely, a deep learning-based framework known as BP-Net is proposed for predicting continuous upper and lower bounds of blood pressure, respectively, known as Systolic BP (SBP) and Diastolic BP (DBP). Furthermore, by capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a unified dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. In the second part, we focus on hand gesture recognition utilizing sEMG signals, which have the potential to be used in the myoelectric prostheses control systems or decoding Myo Armbands data to interpret human intent in AR/VR environments. Capitalizing on the recent advances in hybrid architectures and Transformers in different applications, we aim to enhance the accuracy of sEMG-based hand gesture recognition by introducing a hybrid architecture based on Transformers, referred to as the Transformer for Hand Gesture Recognition (TraHGR). In particular, the TraHGR architecture consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. The ultimate goal of this work is to increase the accuracy of gesture classifications, which could be a major step towards the development of more advanced HMI systems that can improve the quality of life for people with disabilities or enhance the user experience in AR/VR applications. Besides improving accuracy, decreasing the number of parameters in the Deep Neural Network (DNN) architectures plays an important role in wearable devices. In other words, to achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. Therefore, in our next attempt, we propose a lightweight hybrid architecture based on the Convolutional Neural Network (CNN) and attention mechanism, referred to as Hierarchical Depth-wise Convolution along with the Attention Mechanism (HDCAM), to effectively extract local and global representations of the input. The key objective behind the design of HDCAM was to ensure its resource efficiency while maintaining comparable or better performance than the current state-of-the-art methods

    Discovering behavioural patterns using conversational technology for in-home health and well-being monitoring

    Get PDF
    Advancements in conversational AI have created unparalleled opportunities to promote the independence and well-being of older adults, including people living with dementia (PLWD). However, conversational agents have yet to demonstrate a direct impact in supporting target populations at home, particularly with long-term user benefits and clinical utility. We introduce an infrastructure fusing in-home activity data captured by Internet of Things (IoT) technologies with voice interactions using conversational technology (Amazon Alexa). We collect 3103 person-days of voice and environmental data across 14 households with PLWD to identify behavioural patterns. Interactions include an automated well-being questionnaire and 10 topics of interest, identified using topic modelling. Although a significant decrease in conversational technology usage was observed after the novelty phase across the cohort, steady state data acquisition for modelling was sustained. We analyse household activity sequences preceding or following Alexa interactions through pairwise similarity and clustering methods. Our analysis demonstrates the capability to identify individual behavioural patterns, changes in those patterns and the corresponding time periods. We further report that households with PLWD continued using Alexa following clinical events (e.g., hospitalisations), which offers a compelling opportunity for proactive health and well-being data gathering related to medical changes. Results demonstrate the promise of conversational AI in digital health monitoring for ageing and dementia support and offer a basis for tracking health and deterioration as indicated by household activity, which can inform healthcare professionals and relevant stakeholders for timely interventions. Future work will use the bespoke behavioural patterns extracted to create more personalised AI conversations

    Exploring the relationship among stress, psychological wellbeing, and performance in healthcare professionals and healthcare students

    Get PDF
    Background: The prevalence of high stress in healthcare professionals (HCPs) and healthcare students has gained immense attention over the past decade; and more so since the onset of the COVID-19 pandemic. High levels of stress have been shown to have adverse consequences on the psychological wellbeing, and aspects related to performance, in both HCPs and healthcare students, and particularly in the nursing sector. While prior quantitative research has demonstrated a significant relationship among stress, psychological wellbeing and work performance in HCPs worldwide, there is a need to better understand the factors entailed in this dynamic relationship, and gain more in-depth insight from a qualitative perspective too, particularly in nurses working for the National Health Service (NHS). Likewise, such factors warrant further exploration in nursing students too as they comprise a highly stressed population as compared to students in other fields due to the prevalence of complex and challenging issues that start to arise early in their education and training. Besides exploring the various stressors and factors associated with stress and other aspects of psychological wellbeing and performance, it is also important to look into the various coping strategies and resources that are employed in nursing staff and students within the context of stressful situations, including their uptake and views of available wellbeing courses or other sources of support offered within the healthcare settings. Aim: Following an Introduction (Chapter 1) to the key constructs and background pertinent to the current PhD project, the thesis presents a scoping review (Chapter 2) intended to look into psychological wellbeing intervention studies in NHS employees in terms of outcomes associated with improved psychological wellbeing and aspects related to work performance. The next chapter (Chapter 3) presents the core theoretical and practical aspects of the Methodology employed in the three studies which follow: Study 1 (Chapter 4) was intended to cast light into the relationship among stress, psychological wellbeing, aspects of work performance, and coping in nurses working for the NHS. Study 2 (Chapter 5) evaluated the effectiveness and acceptability of an eight-week mindfulness-based cognitive therapy programme (MBCT) delivered to NHS employees. Study 3 (Chapter 6) explored the relationship among perceived stress, coping strategies, emotional intelligence, and self-efficacy in UK nursing students. Finally, the findings across all studies and their implications are integrated in the General Discussion (Chapter 7) wherein an account of the project strengths and limitations is offered along with future directions for research and practice. Method: A mixed-method design was adopted across all three studies for data collection and analysis; quantitative data has been derived through validated self-report questionnaires via an online survey platform. Qualitative data has been collected through semi-structured interviews conducted via Microsoft Teams, including also a series of focus-groups in Study 3. Study 2 adopted a pre-/post- intervention, mixed-methods design, with quantitative assessments obtained at baseline (pre-intervention) and post-intervention and qualitative data obtained through interviews post intervention. Results: In study 1, regression analysis demonstrated a significant positive relationship between stress and impaired work performance; a significant negative relationship between stress and work satisfaction; a significant negative relationship between stress and overall work activity impairment; including a partially mediating role of emotional intelligence between stress and impaired work performance. Thematic analysis revealed the presence of various stressors pre and during the global pandemic, with workload being a major factor; impact of stress on several aspects of psychological wellbeing (in terms of low mood or depression, feelings of frustration, difficulty switching off, anxiety, lifestyle changes, and negative work-life balance); impact of stress on work performance (in terms of inefficient delivery of tasks, poor decision making skills, concentration difficulties, limited attention span, increased errors, forgetting important information, and feelings of frustration towards other colleagues); seeking social support as a major coping mechanism adopted by majority of nurses (from peers, seniors, professionals, and loved ones); receiving support from the organisation (including line managers) although some staff reported lack of adequate support. Uptake of certain wellbeing interventions was reported, but some staff reported lack of awareness and other barriers associated with engagement such as long waiting lists, lack of time, or simply not feeling the need to participate). With regard to study 2, Wilcoxon Signed Rank test findings revealed at post-intervention stage significant reductions in depression and stress, and a significant increase in levels of mindfulness and overall quality of life. Emerged themes reflected beneficial perceived changes in stress and other aspects of psychological wellbeing or state and perceived acceptability of the MBCT programme, while offering recommendations for improvement in future implementation. In study 3, a weak negative correlation was revealed between problem-focused coping and stress; a strong positive correlation between stress and avoidance coping; and no association was found between stress and emotion-focused coping. Emotional intelligence and self-efficacy had a positive significant correlation; a negative association was found between stress and emotional intelligence; and a strong negative correlation between stress and self-efficacy. Finally, a weak positive correlation was found between problem-focused coping and emotional intelligence; and between problem-focused coping and self-efficacy. Further, emotional intelligence did not moderate or mediate the relationship between self-efficacy and stress. Emerged themes highlighted various stressors experienced by nursing students, with balancing between academic and clinical placements being the major source of stress; the impact of stress on psychological wellbeing (in terms of low mood, feelings of demotivation, feelings of frustration with oneself and others around them, feeling overwhelmed difficulties switching off and experiencing low self-esteem); seeking social support as the most common coping strategy (from peers, teachers, university welfare services, professionals, and loved ones); and high perceived self-efficacy. Findings from focus-groups revealed a mixture of problem-focused and emotion-focused coping strategies when placed under stressful academic and placement situations. Conclusions: The combined pattern of quantitative and qualitative findings across all three studies has demonstrated high stress levels among HCPs and students, along with associated negative effects on psychological wellbeing and work or academic performance. However, it also placed emphasis on the significance of personal resources (EI, coping strategies, and self-efficacy) as well as job resources for improving one’s psychological wellbeing and aspects related to one’s work or academic performance. While stress is an inevitable aspect in healthcare settings, the NHS organisations and educational institutions should consider providing enhanced support for improving personal and organisational resources in healthcare staff and students in order to promote or improve their psychological wellbeing and performance

    A review of abnormal behavior detection in activities of daily living

    Get PDF
    Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

    The silenced generation: the “black children” of China’s one-child policy

    Get PDF
    This research illustrates a very little-known social phenomenon of “black children” (hei haizi) who experienced their daily concealment under the one-child policy in China. Challenging existing scholarship of critiquing the state-sanctioned harm against individual families, especially parents’ sufferings and illegal children’s denial status in documents, this research reveals the family as a key figure in distinguishing the “black children” from other “normal” population with the support of state power. It repositions the “black children” as the primary victims of losing their family membership, continued identity, stabilized childhood, reciprocal human respects and freedom in a given society. Details of their lived experiences from day-to-day base was limited touched. The term of “black children” was used to mainly suggest this population’s lack of formal legitimised personhood (hukou registration) in existing studies and documents, however, this research aims at expanding meanings of the label of “black” on levels of formal identity, physical presence, and emotional recognition, so we can have a better understanding what “black” really meant (and still means) to them. This research explains why the “black children” were born, how they were concealed in given families and communities, and what impacts left on their sense making of the identity, belonging, and recognition. Narratives of their displaced childhood, discontinued family membership and disruptive recognition signposts my argument of their triple “black identity” constructed throughout: not only the formal denial against the “black children” on the level of abstract legitimacy, but also family exclusion and social alienation. Furthermore, this generation was not only silenced by the policy’s coercion and family injustice, but also doubly muted by the rapid policy changes in modern China

    Real-time generation and adaptation of social companion robot behaviors

    Get PDF
    Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

    Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review

    Get PDF
    In the digital era, human-robot interaction is rapidly expanding, emphasizing the need for social robots to fluently understand and communicate in multiple languages. It is not merely about decoding words but about establishing connections and building trust. However, many current social robots are limited to popular languages, serving in fields like language teaching, healthcare and companionship. This review examines the AI-driven language abilities in social robots, providing a detailed overview of their applications and the challenges faced, from nuanced linguistic understanding to data quality and cultural adaptability. Last, we discuss the future of integrating advanced language models in robots to move beyond basic interactions and towards deeper emotional connections. Through this endeavor, we hope to provide a beacon for researchers, steering them towards a path where linguistic adeptness in robots is seamlessly melded with their capacity for genuine emotional engagement

    Experiencing Society and the Lived Welfare State

    Get PDF
    This open access book presents a new approach to the history of welfare state. By applying the concepts of experiencing society and the lived welfare state, the collection introduces theoretical, methodological and empirical insights for bridging the everyday life and institutional structures. The chapters analyze how the welfare state as a particular individual-society relationship has become an integral part of living in the modern society. With a long-term perspective, the chapters explore the experience of society which enabled the building and the resilience of a welfare state. As the welfare state is not a universal model of social development but historically unique in different contexts, the book broadens the focus from the Nordic countries to Southern Europe, colonial Asia and post-colonial South America. This collection is essential reading for scholars and students in the social sciences and history, as well as for policymakers and practitioners who face the contemporary and future challenges of the welfare states.publishedVersionPeer reviewe
    • …
    corecore