Phyjama

Nanostructured wearable electrochemical and biosensor towards healthcare management: a review

In recent years, there has been a rapid increase in demand for wearable sensors, particularly these tracking the surroundings, fitness, and health of people. Thus, selective detection in human body fluid is a demand for a smart lifestyle by quick monitoring of electrolytes, drugs, toxins, metabolites and biomolecules, proteins, and the immune system. In this review, these parameters along with the main features of the latest and mostly cited research work on nanostructured wearable electrochemical and biosensors are surveyed. This study aims to help researchers and engineers choose the most suitable selective and sensitive sensor. Wearable sensors have broad and effective sensing platforms, such as contact lenses, Google Glass, skin-patch, mouth gourds, smartwatches, underwear, wristbands, and others. For increasing sensor reliability, additional advancements in electrochemical and biosensor precision, stability in uncontrolled environments, and reproducible sample conveyance are necessary. In addition, the optimistic future of wearable electrochemical sensors in fields, such as remote and customized healthcare and well-being is discussed. Overall, wearable electrochemical and biosensing technologies hold great promise for improving personal healthcare and monitoring performance with the potential to have a significant impact on daily lives. These technologies enable real-time body sensing and the communication of comprehensive physiological information.

Pyjamas, Polysomnography and Professional Athletes: The Role of Sleep Tracking Technology in Sport

Technological advances in sleep monitoring have seen an explosion of devices used to gather important sleep metrics. These devices range from instrumented 'smart pyjamas' through to at-home polysomnography devices. Alongside these developments in sleep technologies, there have been concomitant increases in sleep monitoring in athletic populations, both in the research and in practical settings. The increase in sleep monitoring in sport is likely due to the increased knowledge of the importance of sleep in the recovery process and performance of an athlete, as well as the well-reported challenges that athletes can face with their sleep. This narrative review will discuss: (1) the importance of sleep to athletes; (2) the various wearable tools and technologies being used to monitor sleep in the sport setting; (3) the role that sleep tracking devices may play in gathering information about sleep; (4) the reliability and validity of sleep tracking devices; (5) the limitations and cautions associated with sleep trackers; and, (6) the use of sleep trackers to guide behaviour change in athletes. We also provide some practical recommendations for practitioners working with athletes to ensure that the selection of such devices and technology will meet the goals and requirements of the athlete.

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. "Invisible" wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from "Invisibles" due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40-140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.

End-to-end design of wearable sensors

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

Enabling Longitudinal Respiration Monitoring Using Vapor-Coated Conducting Textiles

Wearable sensors allow for portable, long-term health monitoring in natural environments. Recently, there has been an increase in demand for technology that can reliably monitor respiration, which can be indicative of cardiac diseases, asthma, and infection by respiratory viruses. However, to date, the most reliable respiration monitoring system involves a tightly worn chest belt that is not conducive to longitudinal monitoring. Herein, we report that accurate respiration monitoring can be effected using a fabric-based humidity sensor mounted within a face mask. Our humidity sensor is created using cotton fabrics coated with a persistently p-doped conjugated polymer, poly(3,4-ethylenedioxythiophene):chloride (PEDOT-Cl), using a previously reported chemical vapor deposition process. The vapor-deposited polymer coating displays a stable, rapid, and reversible change in conductivity with an increase in local humidity, such as the humidity changes experienced within a face mask as the wearer breathes. Thus, when integrated into a face mask, the PEDOT-Cl-coated cotton humidity sensor is able to transduce breaths into an electrical signal. The humidity sensor-incorporated face mask is able to differentiate between deep and shallow breathing, as well as breathing versus talking. The sensor-incorporated face mask platform also functions both while walking and sitting, providing equally high signal quality in both indoor and outdoor contexts. Additionally, we show that the face mask can be worn for long periods of time with a negligible decline in the signal quality.

An Overview of Sensors, Design and Healthcare Challenges in Smart Homes: Future Design Questions

The ageing population increases the demand for customized home care. As a result, sensing technologies are finding their way into the home environment. However, challenges associated with how users interact with sensors and data are not well-researched, particularly from a design perspective. This review explores the literature on important research projects around sensors, design and smart healthcare in smart homes, and highlights challenges for design research. A PRISMA protocol-based screening procedure is adopted to identify relevant articles (n = 180) on the subject of sensors, design and smart healthcare. The exploration and analysis of papers are performed using hierarchical charts, force-directed layouts and 'bedraggled daisy' Venn diagrams. The results show that much work has been carried out in developing sensors for smart home care. Less attention is focused on addressing challenges posed by sensors in homes, such as data accessibility, privacy, comfort, security and accuracy, and how design research might solve these challenges. This review raises key design research questions, particularly in working with sensors in smart home environments.

Semantic Data Mining in Ubiquitous Sensing: A Survey

Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

3D Upper Body Reconstruction with Sparse Soft Sensors

Three-dimensional (3D) reconstruction of human body has wide applications, for example, for customized design of clothes and digital avatar production. Existing vision-based systems for 3D body reconstruction require users to wear minimal or extreme-tight clothes in front of cameras, and thus suffer from privacy problems. In this work, we explore a novel solution based on a sparse number of soft sensors on a standard garment, and use it for capturing 3D upper body shape. We utilize the maximal stretching range by modeling the nonlinear performance profile for individual sensors. The body shape can be dynamically reconstructed by analyzing the relationship between mesh deformation and sensor reading, with a learning-based approach. The wearability and flexibility of our prototype allow its use in indoor/outdoor environments and for long-term breath monitoring. Our prototype has been extensively evaluated by multiple users with different body sizes and the same user for multiple days. The results show that our garment prototype is comfortable to wear, and achieves the state-of-the-art reconstruction performance with the advantages in privacy projection and application scenarios.

Continuous Vital Monitoring During Sleep and Light Activity Using Carbon-Black Elastomer Sensors

The comfortable, continuous monitoring of vital parameters is still a challenge. The long-term measurement of respiration and cardiovascular signals is required to diagnose cardiovascular and respiratory diseases. Similarly, sleep quality assessment and the recovery period following acute treatments require long-term vital parameter datalogging. To address these requirements, we have developed “VitalCore”, a wearable continuous vital parameter monitoring device in the form of a T-shirt targeting the uninterrupted monitoring of respiration, pulse, and actigraphy. VitalCore uses polymer-based stretchable resistive bands as the primary sensor to capture breathing and pulse patterns from chest expansion. The carbon black-impregnated polymer is implemented in a U-shaped configuration and attached to the T-shirt with “interfacing” material along with the accompanying electronics. In this paper, VitalCore is bench tested and compared to gold standard respiration and pulse measurements to verify its functionality and further to assess the quality of data captured during sleep and during light exercise (walking). We show that these polymer-based sensors could identify respiratory peaks with a sensitivity of 99.44%, precision of 96.23%, and false-negative rate of 0.557% during sleep. We also show that this T-shirt configuration allows the wearer to sleep in all sleeping positions with a negligible difference of data quality. The device was also able to capture breathing during gait with 88.9–100% accuracy in respiratory peak detection.

Wearable Sensors for Monitoring Human Motion: A Review on Mechanisms, Materials, and Challenges

The emergence of flexible wearable electronics as a new platform for accurate, unobtrusive, user-friendly, and longitudinal sensing has opened new horizons for personalized assistive tools for monitoring human locomotion and physiological signals. Herein, we survey recent advances in methodologies and materials involved in unobtrusively sensing a medium to large range of applied pressures and motions, such as those encountered in large-scale body and limb movements or posture detection. We discuss three commonly used methodologies in human gait studies: inertial, optical, and angular sensors. Next, we survey the various kinds of electromechanical devices (piezoresistive, piezoelectric, capacitive, triboelectric, and transistive) that are incorporated into these sensor systems; define the key metrics used to quantitate, compare, and optimize the efficiency of these technologies; and highlight state-of-the-art examples. In the end, we provide the readers with guidelines and perspectives to address the current challenges of the field.