Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments.

Recently, wearable electronic products and gadgets have developed quickly with the aim of catching up to or perhaps surpassing the ability of human skin to perceive information from the external world, such as pressure and strain. In this study, by first treating the cellulosic fiber (modal textile) substrate with (3-aminopropyl) triethoxysilane (APTES) and then covering it with conductive nanocomposites, a bionic corpuscle layer is produced. The sandwich structure of tactile corpuscle-inspired bionic (TCB) piezoresistive sensors created with the layer-by-layer (LBL) technology consists of a pressure-sensitive module (a bionic corpuscle), interdigital electrodes (a bionic sensory nerve), and a PU membrane (a bionic epidermis). The synergistic mechanism of hydrogen bond and coupling agent helps to improve the adhesive properties of conductive materials, and thus improve the pressure sensitive properties. The TCB sensor possesses favorable sensitivity (1.0005 kPa-1), a wide linear sensing range (1700 kPa), and a rapid response time (40 ms). The sensor is expected to be applied in a wide range of possible applications including human movement tracking, wearable detection system, and textile electronics.

Wearable humidity sensors are attracting strong attention as they allow for real-time and continuous monitoring of important physiological information by enabling activity tracking as well as air quality assessment. Amongst 2Dimensional (2D) materials, graphene oxide (GO) is very attractive for humidity sensing due to its tuneable surface chemistry, high surface area, processability in water, and easy integration onto flexible substrates. However, strong hysteresis, low sensitivity, and cross-sensitivity issues limit the use of GO in practical applications, where continuous monitoring is preferred. Herein, a wearable and wireless impedance-based humidity sensor made with pyrene-functionalized hexagonal boron nitride (h-BN) nanosheets is demonstrated. The device shows enhanced sensitivity towards relative humidity (RH) (>1010 Ohms/%RH in the range from 5% to 100% RH), fast response (0.1 ms), no appreciable hysteresis, and no cross-sensitivity with temperature in the range of 25-60 °C. The h-BN-based sensor is able to monitor the whole breathing cycle process of exhaling and inhaling, hence enabling to record in real-time the subtlest changes of respiratory signals associated with different daily activities as well as various symptoms of flu, without requiring any direct contact with the individual.

Introduction: Body temperature is essential for diagnosing, managing, and following multiple medical conditions. There are several methods and devices to measure body temperature, but most do not allow continuous and prolonged measurement of body temperature. Noninvasive skin temperature sensor combined with a heat flux sensor, also known as the \"double sensor\" technique, is becoming a valuable and simple method for frequently monitoring body temperature. Methods: Body temperature measurements using the \"double sensor\" method in a wearable monitoring device were compared with oral and core body temperature measurements using medical grade thermometers, analyzing data from two prospective clinical trials of different clinical scenarios. One study included 45 hospitalized COVID-19 patients in which oral measurements were taken using a hand-held device, and the second included 18 post-cardiac surgery patients in which rectal measurements were taken using a rectal probe. Results: In study 1, Bland-Altman analysis showed a bias of -0.04°C [0.34-(-0.43)°C, 95% LOA] with a correlation of 99.4% (p < 0.001). In study 2, Bland-Altman analysis showed a bias of 0.0°C [0.27-(-0.28)°C, 95% LOA], and the correlation was 99.3% (p < 0.001). In both studies, stratifying patients based on BMI and skin tone showed high accordance in all sub-groups. Discussion: The wearable monitor showed high correlation with oral and core body temperature measurements in different clinical scenarios.

The development of smart wearable solutions for monitoring daily life health status is increasingly popular, with chest straps and wristbands being predominant. This study introduces a novel sensorized T-shirt design with textile electrodes connected via a knitting technique to a Movesense device. We aimed to investigate the impact of stationary and movement actions on electrocardiography (ECG) and heart rate (HR) measurements using our sensorized T-shirt. Various activities of daily living (ADLs), including sitting, standing, walking, and mopping, were evaluated by comparing our T-shirt with a commercial chest strap. Our findings demonstrate measurement equivalence across ADLs, regardless of the sensing approach. By comparing ECG and HR measurements, we gained valuable insights into the influence of physical activity on sensorized T-shirt development for monitoring. Notably, the ECG signals exhibited remarkable similarity between our sensorized T-shirt and the chest strap, with closely aligned HR distributions during both stationary and movement actions. The average mean absolute percentage error was below 3%, affirming the agreement between the two solutions. These findings underscore the robustness and accuracy of our sensorized T-shirt in monitoring ECG and HR during diverse ADLs, emphasizing the significance of considering physical activity in cardiovascular monitoring research and the development of personal health applications.

BACKGROUND: Surgical procedures involving the hip, knee, or spine represent a majority of orthopaedic procedures performed electively in the health care system. Postoperative care is a key aspect of surgery and mobilisation without injury is the primary objective. Recent advances in wearable technologies allow objective evaluation of walking metrics to inform and guide postoperative care following orthopaedic surgery.
OBJECTIVE: The aim of this scoping review is to explore current applications of wearable devices, objective data capture and gait analysis in monitoring postoperative recovery following commonly performed elective orthopaedic procedures of the hip, knee and spine.
METHODS: A search against pre-defined criteria was performed on the following scientific databases from date of inception to February 28th, 2021: Medline (via OvidSP), Embase (via OvidSP) and Cochrane Library (via CENTRAL). Data were collected according to a predetermined checklist including study participants, surgery, wearable device (model), sensor location, and monitoring parameters such as mobility metrics, monitoring timepoints and monitoring duration for each study included in our review. Quality was assessed independently using the Newcastle Ottawa Scale (NOS).
CONCLUSIONS: To our knowledge, this is the first review of wearable monitoring (of postoperative recovery) following hip, knee and spine surgery. Patients undergoing elective orthopaedic procedures may benefit from wearable monitoring of their walking health and mobility metrics.

Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.

Core body temperature (CBT) is a key vital sign and fever is an important indicator of disease. In the past decade, there has been growing interest for vital sign monitoring technology that may be embedded in wearable devices, and the COVID-19 pandemic has highlighted the need for remote patient monitoring systems. While wrist-worn sensors allow continuous assessment of heart rate and oxygen saturation, reliable measurement of CBT at the wrist remains challenging. In this study, CBT was measured continuously in a free-living setting using a novel technology worn at the wrist and compared to reference core body temperature measurements, i.e., CBT values acquired with an ingestible temperature-sensing pill. Fifty individuals who received the COVID-19 booster vaccination were included. The datasets of 33 individuals were used to develop the CBT prediction algorithm, and the algorithm was then validated on the datasets of 17 participants. Mean observation time was 26.4 h and CBT > 38.0 °C occurred in 66% of the participants. CBT predicted by the wrist-worn sensor showed good correlation to the reference CBT (r = 0.72). Bland-Altman statistics showed an average bias of 0.11 °C of CBT predicted by the wrist-worn device compared to reference CBT, and limits of agreement were - 0.67 to + 0.93 °C, which is comparable to the bias and limits of agreement of commonly used tympanic membrane thermometers. The small size of the components needed for this technology would allow its integration into a variety of wearable monitoring systems assessing other vital signs and at the same time allowing maximal freedom of movement to the user.