Revolutionizing digital healthcare networks with wearable strain sensors using sustainable fibers

Wearable strain sensors have attracted research interest owing to their potential within digital healthcare, offering smarter tracking, efficient diagnostics, and lower costs. Unlike rigid sensors, fiber‐based ones compete with their flexibility, durability, adaptability to body structures as well as eco‐friendliness to environment. Here, the sustainable fiber‐based wearable strain sensors for digital health are reviewed, and material, fabrication, and practical healthcare aspects are explored. Typical strain sensors predicated on various sensing modalities, be it resistive, capacitive, piezoelectric, or triboelectric, are explained and analyzed according to their strengths and weaknesses toward fabrication and applications. The applications in digital healthcare spanning from body area sensing networks, intelligent health management, and medical rehabilitation to multifunctional healthcare systems are also evaluated. Moreover, to create a more complete digital health network, wired and wireless methods of data collection and examples of machine learning are elaborated in detail. Finally, the prevailing challenges and prospective insights into the advancement of novel fibers, enhancement of sensing precision and wearability, and the establishment of seamlessly integrated systems are critically summarized and offered. This endeavor not only encapsulates the present landscape but also lays the foundation for future breakthroughs in fiber‐based wearable strain sensor technology within the domain of digital health

Extending the Design Space of E-textile Assistive Smart Environment Applications

The thriving field of Smart Environments has allowed computing devices to gain new capabilities and develop new interfaces, thus becoming more and more part of our lives. In many of these areas it is unthinkable to renounce to the assisting functionality such as e.g. comfort and safety functions during driving, safety functionality while working in an industrial plant, or self-optimization of daily activities with a Smartwatch. Adults spend a lot of time on flexible surfaces such as in the office chair, in bed or in the car seat. These are crucial parts of our environments. Even though environments have become smarter with integrated computing gaining new capabilities and new interfaces, mostly rigid surfaces and objects have become smarter. In this thesis, I build on the advantages flexible and bendable surfaces have to offer and look into the creation process of assistive Smart Environment applications leveraging these surfaces. I have done this with three main contributions. First, since most Smart Environment applications are built-in into rigid surfaces, I extend the body of knowledge by designing new assistive applications integrated in flexible surfaces such as comfortable chairs, beds, or any type of soft, flexible objects. These developed applications offer assistance e.g. through preventive functionality such as decubitus ulcer prevention while lying in bed, back pain prevention while sitting on a chair or emotion detection while detecting movements on a couch. Second, I propose a new framework for the design process of flexible surface prototypes and its challenges of creating hardware prototypes in multiple iterations, using resources such as work time and material costs. I address this research challenge by creating a simulation framework which can be used to design applications with changing surface shape. In a first step I validate the simulation framework by building a real prototype and a simulated prototype and compare the results in terms of sensor amount and sensor placement. Furthermore, I use this developed simulation framework to analyse the influence it has on an application design if the developer is experienced or not. Finally, since sensor capabilities play a major role during the design process, and humans come often in contact with surfaces made of fabric, I combine the integration advantages of fabric and those of capacitive proximity sensing electrodes. By conducting a multitude of capacitive proximity sensing measurements, I determine the performance of electrodes made by varying properties such as material, shape, size, pattern density, stitching type, or supporting fabric. I discuss the results from this performance evaluation and condense them into e-textile capacitive sensing electrode guidelines, applied exemplary on the use case of creating a bed sheet for breathing rate detection

A NEUROMORPHIC APPROACH TO TACTILE PERCEPTION

Ph.DDOCTOR OF PHILOSOPH

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

none3noDigital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.Article Number: 1660openRoberto De Fazio; Massimo De Vittorio; Paolo ViscontiDE FAZIO, Roberto; DE VITTORIO, Massimo; Visconti, Paol

Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Tailoring Interaction. Sensing Social Signals with Textiles.

Nonverbal behaviour is an important part of conversation and can reveal much about the nature of an interaction. It includes phenomena ranging from large-scale posture shifts to small scale nods. Capturing these often spontaneous phenomena requires unobtrusive sensing techniques that do not interfere with the interaction. We propose an underexploited sensing modality for sensing nonverbal behaviours: textiles. As a material in close contact with the body, they provide ubiquitous, large surfaces that make them a suitable soft interface. Although the literature on nonverbal communication focuses on upper body movements such as gestures, observations of multi-party, seated conversations suggest that sitting postures, leg and foot movements are also systematically related to patterns of social interaction. This thesis addressees the following questions: Can the textiles surrounding us measure social engagement? Can they tell who is speaking, and who, if anyone, is listening? Furthermore, how should wearable textile sensing systems be designed and what behavioural signals could textiles reveal? To address these questions, we have designed and manufactured bespoke chairs and trousers with integrated textile pressure sensors, that are introduced here. The designs are evaluated in three user studies that produce multi-modal datasets for the exploration of fine-grained interactional signals. Two approaches to using these bespoke textile sensors are explored. First, hand crafted sensor patches in chair covers serve to distinguish speakers and listeners. Second, a pressure sensitive matrix in custom-made smart trousers is developed to detect static sitting postures, dynamic bodily movement, as well as basic conversational states. Statistical analyses, machine learning approaches, and ethnographic methods show that by moni- toring patterns of pressure change alone it is possible to not only classify postures with high accuracy, but also to identify a wide range of behaviours reliably in individuals and groups. These findings es- tablish textiles as a novel, wearable sensing system for applications in social sciences, and contribute towards a better understanding of nonverbal communication, especially the significance of posture shifts when seated. If chairs know who is speaking, if our trousers can capture our social engagement, what role can smart textiles have in the future of human interaction? How can we build new ways to map social ecologies and tailor interactions