Switched Uses of a Bidirectional Microphone as a Microphone and Sensors with High Gain and Wide Frequency Range

Mass-produced bidirectional microphones have attractive characteristics. They work as a microphone as well as a sensor with high gain over a wide frequency range; they are also highly reliable and economical. We present novel multiple functional uses of the microphones. A mathematical model for explaining the directivity and high-pass-filtering characteristics of bidirectional microphones was presented. Based on the model, the characteristics of the microphone were investigated, and a novel use for the microphone as a sensor with a wide frequency range was presented. In this study, applications for using the microphone as a security sensor, an environment sensor, and a human biosensor were introduced. The mathematical model was validated through experiments, and the feasibility of the abovementioned applications for security monitoring, environment monitoring, and the biosignal monitoring were examined through experiments.修士(工学)法政大学 (Hosei University

Mulsemedia Communication Research Challenges for Metaverse in 6G Wireless Systems

Although humans have five basic senses, sight, hearing, touch, smell, and taste, most multimedia systems in current systems only capture two of them, namely, sight and hearing. With the development of the metaverse and related technologies, there is a growing need for a more immersive media format that leverages all human senses. Multisensory media(Mulsemedia) that can stimulate multiple senses will play a critical role in the near future. This paper provides an overview of the history, background, use cases, existing research, devices, and standards of mulsemedia. Emerging mulsemedia technologies such as Extended Reality (XR) and Holographic-Type Communication (HTC) are introduced. Additionally, the challenges in mulsemedia research from the perspective of wireless communication and networking are discussed. The potential of 6G wireless systems to address these challenges is highlighted, and several research directions that can advance mulsemedia communications are identified

Machine learning to model health with multimodal mobile sensor data

The widespread adoption of smartphones and wearables has led to the accumulation of rich datasets, which could aid the understanding of behavior and health in unprecedented detail. At the same time, machine learning and specifically deep learning have reached impressive performance in a variety of prediction tasks, but their use on time-series data appears challenging. Existing models struggle to learn from this unique type of data due to noise, sparsity, long-tailed distributions of behaviors, lack of labels, and multimodality. This dissertation addresses these challenges by developing new models that leverage multi-task learning for accurate forecasting, multimodal fusion for improved population subtyping, and self-supervision for learning generalized representations. We apply our proposed methods to challenging real-world tasks of predicting mental health and cardio-respiratory fitness through sensor data. First, we study the relationship of passive data as collected from smartphones (movement and background audio) to momentary mood levels. Our new training pipeline, which combines different sensor data into a low-dimensional embedding and clusters longitudinal user trajectories as outcome, outperforms traditional approaches based solely on psychology questionnaires. Second, motivated by mood instability as a predictor of poor mental health, we propose encoder-decoder models for time-series forecasting which exploit the bi-modality of mood with multi-task learning. Next, motivated by the success of general-purpose models in vision and language tasks, we propose a self-supervised neural network ready-to-use as a feature extractor for wearable data. To this end, we set the heart rate responses as the supervisory signal for activity data, leveraging their underlying physiological relationship and show that the resulting task-agnostic embeddings can generalize in predicting structurally different downstream outcomes through transfer learning (e.g. BMI, age, energy expenditure), outperforming unsupervised autoencoders and biomarkers. Finally, acknowledging fitness as a strong predictor of overall health, which, however, can only be measured with expensive instruments (e.g., a VO2max test), we develop models that enable accurate prediction of fine-grained fitness levels with wearables in the present, and more importantly, its direction and magnitude almost a decade later. All proposed methods are evaluated on large longitudinal datasets with tens of thousands of participants in the wild. The models developed and the insights drawn in this dissertation provide evidence for a better understanding of high-dimensional behavioral and physiological data with implications for large-scale health and lifestyle monitoring.The Department of Computer Science and Technology at the University of Cambridge through the EPSRC through Grant DTP (EP/N509620/1), and the Embiricos Trust Scholarship of Jesus College Cambridg

Enriching mobile interaction with garment-based wearable computing devices

Wearable computing is on the brink of moving from research to mainstream. The first simple products, such as fitness wristbands and smart watches, hit the mass market and achieved considerable market penetration. However, the number and versatility of research prototypes in the field of wearable computing is far beyond the available devices on the market. Particularly, smart garments as a specific type of wearable computer, have high potential to change the way we interact with computing systems. Due to the proximity to the user`s body, smart garments allow to unobtrusively sense implicit and explicit user input. Smart garments are capable of sensing physiological information, detecting touch input, and recognizing the movement of the user. In this thesis, we explore how smart garments can enrich mobile interaction. Employing a user-centered design process, we demonstrate how different input and output modalities can enrich interaction capabilities of mobile devices such as mobile phones or smart watches. To understand the context of use, we chart the design space for mobile interaction through wearable devices. We focus on the device placement on the body as well as interaction modality. We use a probe-based research approach to systematically investigate the possible inputs and outputs for garment based wearable computing devices. We develop six different research probes showing how mobile interaction benefits from wearable computing devices and what requirements these devices pose for mobile operating systems. On the input side, we look at explicit input using touch and mid-air gestures as well as implicit input using physiological signals. Although touch input is well known from mobile devices, the limited screen real estate as well as the occlusion of the display by the input finger are challenges that can be overcome with touch-enabled garments. Additionally, mid-air gestures provide a more sophisticated and abstract form of input. We present a gesture elicitation study to address the special requirements of mobile interaction and present the resulting gesture set. As garments are worn, they allow different physiological signals to be sensed. We explore how we can leverage these physiological signals for implicit input. We conduct a study assessing physiological information by focusing on the workload of drivers in an automotive setting. We show that we can infer the driver´s workload using these physiological signals. Beside the input capabilities of garments, we explore how garments can be used as output. We present research probes covering the most important output modalities, namely visual, auditory, and haptic. We explore how low resolution displays can serve as a context display and how and where content should be placed on such a display. For auditory output, we investigate a novel authentication mechanism utilizing the closeness of wearable devices to the body. We show that by probing audio cues through the head of the user and re-recording them, user authentication is feasible. Last, we investigate EMS as a haptic feedback method. We show that by actuating the user`s body, an embodied form of haptic feedback can be achieved. From the aforementioned research probes, we distilled a set of design recommendations. These recommendations are grouped into interaction-based and technology-based recommendations and serve as a basis for designing novel ways of mobile interaction. We implement a system based on these recommendations. The system supports developers in integrating wearable sensors and actuators by providing an easy to use API for accessing these devices. In conclusion, this thesis broadens the understanding of how garment-based wearable computing devices can enrich mobile interaction. It outlines challenges and opportunities on an interaction and technological level. The unique characteristics of smart garments make them a promising technology for making the next step in mobile interaction

A heterogeneous network management approach to wireless sensor networks in personal healthcare environments

University of Technology, Sydney. Faculty of Science.Many countries are facing problems caused by a rapid surge in numbers of people over sixty-five. This aging population cohort will place a strain on the existing health systems because the elderly are prone to falls, chronic illnesses, dementia and general frailty. At the same time governments are struggling to attract more people into the health systems and there are already shortages of qualified nurses and care givers. This thesis represents a multi disciplinary approach to trying to solve some of the above issues. In the first instance the researcher has established the validity of the health crisis and then examined ways in which Information Technology could help to alleviate some of the issues. The nascent technology called Wireless Sensor Networks was examined as a way of providing remote health monitoring for the elderly, the infirm and the ill. The researcher postulated that Network Management models and tools that are used to monitor huge networks of computers could be adapted to monitor the health of persons in their own homes, in aged care facilities and hospitals. Wireless Sensor Network (WNS) Personal Healthcare can monitor such vital signs as a patient’s temperature, heart rate and blood oxygen level. WSNs (often referred to as Motes) use wireless transceivers that can do remote sensing. The researcher aimed to assist all stakeholders in the personal healthcare arena to use WSNs to improve monitoring. The researcher provided a solution architecture and framework for healthcare sensor monitoring systems, based on network management techniques. This architecture generalises to heterogeneous and autonomous data acquisition systems. Future directions from this research point towards new areas of knowledge from the development or creation of new technologies to support the exponential growth of ubiquitous, just-in-time WSN health informational services and applications such as the preventive and proactive personal care health management and services around it. The affordable and ubiquitous distributed access to remote personal health care technologies in the future could have an important impact in the society, by allowing the individuals to take immediate preventive actions over their overall health condition. These systems could potentially prevent death as well as improve national health budgets by limiting costly medical interventions that could have been avoided by individual, easy-action early prevention

NON-VERBAL COMMUNICATION WITH PHYSIOLOGICAL SENSORS. THE AESTHETIC DOMAIN OF WEARABLES AND NEURAL NETWORKS

Historically, communication implies the transfer of information between bodies, yet this phenomenon is constantly adapting to new technological and cultural standards. In a digital context, it’s commonplace to envision systems that revolve around verbal modalities. However, behavioural analysis grounded in psychology research calls attention to the emotional information disclosed by non-verbal social cues, in particular, actions that are involuntary. This notion has circulated heavily into various interdisciplinary computing research fields, from which multiple studies have arisen, correlating non-verbal activity to socio-affective inferences. These are often derived from some form of motion capture and other wearable sensors, measuring the ‘invisible’ bioelectrical changes that occur from inside the body. This thesis proposes a motivation and methodology for using physiological sensory data as an expressive resource for technology-mediated interactions. Initialised from a thorough discussion on state-of-the-art technologies and established design principles regarding this topic, then applied to a novel approach alongside a selection of practice works to compliment this. We advocate for aesthetic experience, experimenting with abstract representations. Atypically from prevailing Affective Computing systems, the intention is not to infer or classify emotion but rather to create new opportunities for rich gestural exchange, unconfined to the verbal domain. Given the preliminary proposition of non-representation, we justify a correspondence with modern Machine Learning and multimedia interaction strategies, applying an iterative, human-centred approach to improve personalisation without the compromising emotional potential of bodily gesture. Where related studies in the past have successfully provoked strong design concepts through innovative fabrications, these are typically limited to simple linear, one-to-one mappings and often neglect multi-user environments; we foresee a vast potential. In our use cases, we adopt neural network architectures to generate highly granular biofeedback from low-dimensional input data. We present the following proof-of-concepts: Breathing Correspondence, a wearable biofeedback system inspired by Somaesthetic design principles; Latent Steps, a real-time auto-encoder to represent bodily experiences from sensor data, designed for dance performance; and Anti-Social Distancing Ensemble, an installation for public space interventions, analysing physical distance to generate a collective soundscape. Key findings are extracted from the individual reports to formulate an extensive technical and theoretical framework around this topic. The projects first aim to embrace some alternative perspectives already established within Affective Computing research. From here, these concepts evolve deeper, bridging theories from contemporary creative and technical practices with the advancement of biomedical technologies.Historicamente, os processos de comunicação implicam a transferência de informação entre organismos, mas este fenómeno está constantemente a adaptar-se a novos padrões tecnológicos e culturais. Num contexto digital, é comum encontrar sistemas que giram em torno de modalidades verbais. Contudo, a análise comportamental fundamentada na investigação psicológica chama a atenção para a informação emocional revelada por sinais sociais não verbais, em particular, acções que são involuntárias. Esta noção circulou fortemente em vários campos interdisciplinares de investigação na área das ciências da computação, dos quais surgiram múltiplos estudos, correlacionando a actividade nãoverbal com inferências sócio-afectivas. Estes são frequentemente derivados de alguma forma de captura de movimento e sensores “wearable”, medindo as alterações bioeléctricas “invisíveis” que ocorrem no interior do corpo. Nesta tese, propomos uma motivação e metodologia para a utilização de dados sensoriais fisiológicos como um recurso expressivo para interacções mediadas pela tecnologia. Iniciada a partir de uma discussão aprofundada sobre tecnologias de ponta e princípios de concepção estabelecidos relativamente a este tópico, depois aplicada a uma nova abordagem, juntamente com uma selecção de trabalhos práticos, para complementar esta. Defendemos a experiência estética, experimentando com representações abstractas. Contrariamente aos sistemas de Computação Afectiva predominantes, a intenção não é inferir ou classificar a emoção, mas sim criar novas oportunidades para uma rica troca gestual, não confinada ao domínio verbal. Dada a proposta preliminar de não representação, justificamos uma correspondência com estratégias modernas de Machine Learning e interacção multimédia, aplicando uma abordagem iterativa e centrada no ser humano para melhorar a personalização sem o potencial emocional comprometedor do gesto corporal. Nos casos em que estudos anteriores demonstraram com sucesso conceitos de design fortes através de fabricações inovadoras, estes limitam-se tipicamente a simples mapeamentos lineares, um-para-um, e muitas vezes negligenciam ambientes multi-utilizadores; com este trabalho, prevemos um potencial alargado. Nos nossos casos de utilização, adoptamos arquitecturas de redes neurais para gerar biofeedback altamente granular a partir de dados de entrada de baixa dimensão. Apresentamos as seguintes provas de conceitos: Breathing Correspondence, um sistema de biofeedback wearable inspirado nos princípios de design somaestético; Latent Steps, um modelo autoencoder em tempo real para representar experiências corporais a partir de dados de sensores, concebido para desempenho de dança; e Anti-Social Distancing Ensemble, uma instalação para intervenções no espaço público, analisando a distância física para gerar uma paisagem sonora colectiva. Os principais resultados são extraídos dos relatórios individuais, para formular um quadro técnico e teórico alargado para expandir sobre este tópico. Os projectos têm como primeiro objectivo abraçar algumas perspectivas alternativas às que já estão estabelecidas no âmbito da investigação da Computação Afectiva. A partir daqui, estes conceitos evoluem mais profundamente, fazendo a ponte entre as teorias das práticas criativas e técnicas contemporâneas com o avanço das tecnologias biomédicas

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Sensing via signal analysis, analytics, and cyberbiometric patterns

Includes bibliographical references.2022 Fall.Internet-connected, or Internet of Things (IoT), sensor technologies have been increasingly incorporated into everyday technology and processes. Their functions are situationally dependent and have been used for vital recordings such as electrocardiograms, gait analysis and step counting, fall detection, and environmental analysis. For instance, environmental sensors, which exist through various technologies, are used to monitor numerous domains, including but not limited to pollution, water quality, and the presence of biota, among others. Past research into IoT sensors has varied depending on the technology. For instance, previous environmental gas sensor IoT research has focused on (i) the development of these sensors for increased sensitivity and increased lifetimes, (ii) integration of these sensors into sensor arrays to combat cross-sensitivity and background interferences, and (iii) sensor network development, including communication between widely dispersed sensors in a large-scale environment. IoT inertial measurement units (IMU's), such as accelerometers and gyroscopes, have been previously researched for gait analysis, movement detection, and gesture recognition, which are often related to human-computer interface (HCI). Methods of IoT Device feature-based pattern recognition for machine learning (ML) and artificial intelligence (AI) are frequently investigated as well, including primitive classification methods and deep learning techniques. The result of this research gives insight into each of these topics individually, i.e., using a specific sensor technology to detect carbon monoxide in an indoor environment, or using accelerometer readings for gesture recognition. Less research has been performed on analyzing the systems aspects of the IoT sensors themselves. However, an important part of attaining overall situational awareness is authenticating the surroundings, which in the case of IoT means the individual sensors, humans interacting with the sensors, and other elements of the surroundings. There is a clear opportunity for the systematic evaluation of the identity and performance of an IoT sensor/sensor array within a system that is to be utilized for "full situational awareness". This awareness may include (i) non-invasive diagnostics (i.e., what is occurring inside the body), (ii) exposure analysis (i.e., what has gone into the body through both respiratory and eating/drinking pathways), and (iii) potential risk of exposure (i.e., what the body is exposed to environmentally). Simultaneously, the system has the capability to harbor security measures through the same situational assessment in the form of multiple levels of biometrics. Through the interconnective abilities of the IoT sensors, it is possible to integrate these capabilities into one portable, hand-held system. The system will exist within a "magic wand", which will be used to collect the various data needed to assess the environment of the user, both inside and outside of their bodies. The device can also be used to authenticate the user, as well as the system components, to discover potential deception within the system. This research introduces levels of biometrics for various scenarios through the investigation of challenge-based biometrics; that is, biometrics based upon how the sensor, user, or subject of study responds to a challenge. These will be applied to multiple facets surrounding "situational awareness" for living beings, non-human beings, and non-living items or objects (which we have termed "abiometrics"). Gesture recognition for intent of sensing was first investigated as a means of deliberate activation of sensors/sensor arrays for situational awareness while providing a level of user authentication through biometrics. Equine gait analysis was examined next, and the level of injury in the lame limbs of the horse was quantitatively measured and classified using data from IoT sensors. Finally, a method of evaluating the identity and health of a sensor/sensory array was examined through different challenges to their environments

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

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