Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Design Strategies For Human Centered Sensor Networks

Sensor networks hold great promise in a variety of applications, and have been the subject of a great deal of active research in recent years. Similar to large scale computer networks, such as the internet, sensor networks are in essence information gathering mechanisms. In addition, sensor networks directly gather information about their environment, making this information available at what could be considered an extremely low cost. This feature is in part responsible for their wide applicability, but also makes sensor networks fundamentally different from other network technologies. This dissertation examines the construction of two sensor network systems, and asserts that, among sensor network applications, there is a critical distinction between those which are human-centered and non-human-centered. The first of these two particular systems used sensors embedded in an indoor environment to assess the movement of persons throughout the space. The second is the development of a medically oriented system which includes primarily wearable sensors. Through a discussion of their design and construction, we will distill design strategies for constructing such systems, particularly noting features which separate human-centered and non-human-centered systems

PhysioKit: An Open-Source, Low-Cost Physiological Computing Toolkit for Single- and Multi-User Studies

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit provides a one-stop pipeline consisting of (i) a sensing and data acquisition layer that can be configured in a modular manner per research needs, and (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study with 16 participants, PhysioKit shows strong agreement with research-grade sensors on measuring heart rate and heart rate variability metrics data. Furthermore, we report usability survey results from 10 small-project teams (44 individual members in total) who used PhysioKit for 4–6 weeks, providing insights into its use cases and research benefits. Lastly, we discuss the extensibility and potential impact of the toolkit on the research community

Electronic Skin in Robotics and Healthcare: Towards Multimodal Sensing and Intelligent Analysis

Skin-interfaced electronics is gradually transforming robotic and medical fields by enabling noninvasive and continuous monitoring of physiological and biochemical information. Despite their promise, current wearable technologies face challenges in several disciplines: Physical sensors are prone to motion-induced noise and lack the capability for effective disease detection, while existing wearable biochemical sensors suffer from operational instability in biofluids, limiting their practicality. Conventional electronic skin contains only a limited category of sensors that are not sufficient for practical applications, and conventional data processing methods for these wearables necessitate manual intervention to filter noise and decipher health-related information. This thesis presents advances in electronic skin within robotics and healthcare, emphasizing multimodal sensing and data analysis through machine intelligence. Chapter 1 introduces the concept of electronic skin, outlining the emerging sensor technologies and a general machine learning pipeline for data processing. Chapter 2 details the development of multimodal physiological and biochemical sensors that enable long-term continuous monitoring with high sensitivity and stability. Chapter 3 explores the application of integrated electronic skin in robotics, prosthetics, and human machine interactions. Chapter 4 showcases practical implementations of integrated electronic skin with robust sensors for wound monitoring and treatment. Chapter 5 highlights the transformative deployment of artificial intelligence in deconvoluting health profiles on mental health. The last chapter, Chapter 6, delves into the challenges and prospects of artificial intelligence-powered electronic skins, offering predictions for the evolution of smart electronic skins. We envision that multimodal sensing and machine intelligence in electronic skin could significantly advance the field of human machine interactions and personalized healthcare.</p

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

Towards Stable Electrochemical Sensing for Wearable Wound Monitoring

Wearable biosensing has the tremendous advantage of providing point-of-care diagnosis and convenient therapy. In this research, methods to stabilize the electrochemical sensing response from detection of target biomolecules, Uric Acid (UA) and Xanthine, closely linked to wound healing, have been investigated. Different kinds of materials have been explored to address such detection from a wearable, healing platform. Electrochemical sensing modalities have been implemented in the detection of purine metabolites, UA and Xanthine, in the physiologically relevant ranges of the respective biomarkers. A correlation can be drawn between the concentrations of these bio-analytes and wound severity, thus offering probable quantitative insights on wound healing progression. These insights attempt to contribute in reducing some impacts of the financial structure on the healthcare economy associated with wound-care. An enzymatic electrochemical sensing system was designed to provide quick response at a cost-effective, miniaturized scale. Robust enzyme immobilization protocols have assisted in preserving enzyme activity to offer stable response under relevant variations of temperature and pH, from normal. Increased hydrophilicity of the sensor surface using corona plasma, has assisted in improving conductivity, thus allowing for increased electroactive functionalization and loading across the substrate’s surface. Superior sensor response was attained from higher loading of nanomaterials (MWCNT/AuNP) and enzymes (UOx/XO) employed in detection. Potentiometric analyses of the nanomaterial modified enzymatic biosensors were conducted using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) modalities. Under relevant physiological conditions, the biosensor was noted to offer a variation in response between 10 % and 30 % within a week. Stable, reproducible results were obtained from repeated use of the biosensor over multiple days, also offering promise for continuous monitoring. Shelf life of the biosensor was noted to be more than two days with response retained by about 80 % thereafter. Secondary analyses have been performed utilizing the enzymatic biosensor to explore the feasibility of target biomarker detection from clinical extracts of different biofluids for wound monitoring. Biosensor response evaluation from the extracts of human wound exudate, and those obtained from perilesional and healthy skin, provided an average recovery between 107 % and 110 % with a deviation within (+/-) 6 %. Gradual decrease in response (10-20 %) was noted in detection from extracts further away from injury site. Increased accumulation of biofluids on the sensor surface was studied to explore sensor response stability as a function of sample volume. With a broad linear range of detection (0.1 nM – 7.3 mM) and detection limits lower than the physiological concentrations, this study has assessed the viability of stable wound monitoring under physiologically relevant conditions on a wearable platform

Plant disease detections using deep learning techniques

Istraživanja predstavljena u disertaciji imala su za cilj razvoj nove metode bazirane na dubokim konvolucijskim neuoronskim mrežama u cilju detekcije bolesti biljaka na osnovu slike lista. U okviru eksperimentalnog dela rada prikazani su dosadašnji literaturno dostupni pristupi u automatskoj detekciji bolesti biljaka kao i ograničenja ovako dobijenih modela kada se koriste u prirodnim uslovima. U okviru disertacije uvedena je nova baza slika listova, trenutno najveća po broju slika u poređenju sa javno dostupnim bazama, potvrđeni su novi pristupi augmentacije bazirani na GAN arhitekturi nad slikama listova uz novi specijalizovani dvo-koračni pristup kao potencijalni odgovor na nedostatke postojećih rešenja.The research presented in this thesis was aimed at developing a novel method based on deep convolutional neural networks for automated plant disease detection. Based on current available literature, specialized two-phased deep neural network method introduced in the experimental part of thesis solves the limitations of state-of-the-art plant disease detection methods and provides the possibility for a practical usage of the newly developed model. In addition, a new dataset was introduced, that has more images of leaves than other publicly available datasets, also GAN based augmentation approach on leaves images is experimentally confirmed

Plant disease detections using deep learning techniques

Istraživanja predstavljena u disertaciji imala su za cilj razvoj nove metode bazirane na dubokim konvolucijskim neuoronskim mrežama u cilju detekcije bolesti biljaka na osnovu slike lista. U okviru eksperimentalnog dela rada prikazani su dosadašnji literaturno dostupni pristupi u automatskoj detekciji bolesti biljaka kao i ograničenja ovako dobijenih modela kada se koriste u prirodnim uslovima. U okviru disertacije uvedena je nova baza slika listova, trenutno najveća po broju slika u poređenju sa javno dostupnim bazama, potvrđeni su novi pristupi augmentacije bazirani na GAN arhitekturi nad slikama listova uz novi specijalizovani dvo-koračni pristup kao potencijalni odgovor na nedostatke postojećih rešenja.The research presented in this thesis was aimed at developing a novel method based on deep convolutional neural networks for automated plant disease detection. Based on current available literature, specialized two-phased deep neural network method introduced in the experimental part of thesis solves the limitations of state-of-the-art plant disease detection methods and provides the possibility for a practical usage of the newly developed model. In addition, a new dataset was introduced, that has more images of leaves than other publicly available datasets, also GAN based augmentation approach on leaves images is experimentally confirmed

Combining wearables and nearables for patient state analysis

Recently, ambient patient monitoring using wearable and nearable sensors is becoming more prevalent, especially in the neurodegenerative (Rett syndrome) and sleep disorder (Obstructive sleep apnea) populations. While wearables capture localized physiological data such as pulse rate, wrist acceleration and brain signals, nearables record global passive data including body movements, ambient sound and environmental variables. Together, wearables and nearables provide a more comprehensive understanding of the patient state. The processing of data captured from wearables and nearables have multiple challenges including handling missing data, time synchronization between sensors and developing data fusion techniques for multimodal analysis. The research described in this thesis addresses these issues while working on data captured in the wild. First, we describe a Rett syndrome severity estimator using a wearable biosensor and uncover physio-motor biomarkers. Second, we present the applications of an edge computing and ambient data capture system for home and clinical environments. Finally, we describe a transfer learning and multimodal data fusion based sleep-wake detector for a mixed-disorder elderly population. We show that combining data from wearables and nearables improves the performance of sleep-wake detection in terms of the F1-score and the Cohen’s kappa compared to the unimodal models.Ph.D