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Long COVID, or post-acute sequelae of COVID-19 (PASC), represents a complex condition with persistent symptoms following SARS-Cov-2 infection. The symptoms include fatigue, dyspnoea, cognitive impairment, decreased quality of life in variable levels of severity. Potential mechanisms behind long COVID include vascular damage, immune dysregulation and viral persistence. Diagnosing long COVID involves medical evaluation by multidisciplinary team and assessment of persistent symptoms with scoring systems in development. Treatment strategies are symptom-focused, encompassing multidisciplinary care, rehabilitation and tailored exercise programmes. Pulmonary rehabilitation, an effective and critical component of long COVID management, has shown promise, particularly for patients with respiratory symptoms such as dyspnoea. These programmes, which combine exercise, breathing techniques, education and psychological support, improve symptoms, quality of life and overall recovery. Innovative technologies, such as telemedicine, wearable devices, telerehabilitation, are transforming long COVID management. Telemedicine facilitates consultations and interventions, eliminating healthcare access barriers. Wearable devices enable remote and continuous monitoring of patients during their rehabilitation activities. Telerehabilitation has proven to be safe and feasible and to have high potential for COVID-19 recovery. This review provides a concise overview of long COVID, encompassing its definition, prevalence, mechanisms, clinical manifestations, diagnosis and management approaches. It emphasizes the significance of multidisciplinary approach in diagnosis and treatment of long COVID, with focus on pulmonary rehabilitation and innovative technology advances to effectively address the management of long COVID.

Soft strain sensors can accurately monitor the deformation of irregular and dynamic surfaces. However, traditional thin‐film strain sensors are generally not direction‐specific, and the sensor becomes unpredictable when stretched in different directions simultaneously. Here, a soft 1D strain sensor is reported that is only sensitive to axial deformation by rolling up a thin‐film strain sensor based on liquid metals (LM) and thermoplastic polyurethane (TPU) elastomer. The sensitivity of the 1D sensor can be regulated by rolling the 2D sensor at different angles. The 1D strain sensors have a large strain measuring range (>100%), high strain resolution (0.5%), fast responsiveness (<40 ms), and high reproducibility (over 2000 cycles), which are expected to adapt to various application scenarios with different sensitivity requirements. In addition, 1D strain sensors with different sensitivities can be braided into a three‐strand braid to provide measurements with three different sensitivities at the same time. [ABSTRACT FROM AUTHOR]

The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have been actively researched. The threat analysis framework used in existing studies was limited to specific protocols and did not target IoWT devices. In addition, In the literature surveyed to date, no cyber threat analysis framework is targeting IoWT. Therefore, the threat model presented in the existing research on cyber threat analysis and modeling for IoWT is specialized for specific devices. In addition, because it does not present standardized attack tactics and techniques, there is a limitation in that it is difficult to identify attacks quickly. In this paper, we propose an Internet of Wearable Things threat analysis frameWork (IWTW) framework that can derive security threats through systematic analysis of IoWT attack cases and possible security threats and perform cyber threat analysis based on them. The methodology for developing the IWTW framework consists of three steps: Analysis, Standardization, and Compilation. IoWT attack cases and potential security threats are analyzed in the analysis stage. In the standardization stage, attack tactics and techniques derived from the analysis of attack cases and potential security threats are standardized, resulting in 3 attack categories, 18 attack tactics, and 68 attack techniques. In the compilation stage, standardized security threats are combined to develop the IWTW framework ultimately. We present four case studies targeting MiBand 2, Fitbit Charge HR/Surge, Samsung Gear 3, Xiaomi Amazifit, Honor Band 5, Honor Watch ES, and Senbono CF-58 devices to validate the proposed IWTW framework. We analyzed the attack process through a case study and applied the IWTW framework to derive standardized attack categories, tactics, and techniques effectively. By applying the IWTW framework to cyber threat analysis targeting IoWT, security threats can be standardized, and the attack process can be quickly derived, enabling effective attack analysis on IoWT. [ABSTRACT FROM AUTHOR]

IMU sensors (three-dimensional accelerometer, gyroscope and magnetometer) enable assessment of walking in older adults outside the laboratory. We studied whether IMUs are valid for detecting walking parameters (step events, time, length, and cadence) in a laboratory and outdoors on a level surface in older adults. This validation study is part of a larger cross-sectional study. Twenty-six participants (mean age 76 years, 65 % female) walked on a treadmill indoors and on a sport track outdoors at self-selected speed. IMUs were attached laterally on the shanks and on the lower back at the level of L3-L4. Initial contact (IC) and step lengths were also estimated using acceleration signals (vertical, antero-posterior) from the pelvic IMU. Terminal contact (TC) was determined from the shank IMU sagittal angular velocity. For step length, inverted pendulum model and participant's leg length (0.53 x height) was used. Step duration was calculated from IC to the opposite leg IC and stride duration from IC to next ipsilateral IC. Cadence was calculated as steps/min. As reference data, 3D motion capture was used in the laboratory and a high-speed video camera outdoors. Intraclass correlation coefficients (ICC), root mean squared errors (RMSE), typical errors and Bland-Altman plots were calculated and drawn. When comparing IC timing between IMU and reference data, mean bias was 0.031 s in the laboratory and −0.004 s outdoors, and for TC −0.057 s and −0.070 s respectively. Step and stride duration and cadence showed ICC values >0.80 and mean bias was <0.005 s for step and stride durations and <0.05 steps/min for cadence in both environments. Step length ICC values were <0.40 in the laboratory and outdoors. IMUs can be used to monitor temporal walking variables in older adults and may be useful for rehabilitation interventions and functional capacity assessment. • Older adults' walking parameters can be assessed also outdoors with IMU sensors • Accuracy of step/stride duration and cadence is good outdoors and in the laboratory • Accuracy of step length and walking speed is poorer in both environments [ABSTRACT FROM AUTHOR]

Assessing the effect of insoles on gait biomechanics and foot comfort remains challenging. Our novel in-insole-type wearable sensor device (smart insole) enables accurate quantitative evaluation of gait parameters without affecting the subject's foot comfort. What are the effects of insoles on gait biomechanics and foot comfort in patients with flatfoot, as evaluated using a novel smart insole? Thirty-three subjects with 61 flatfeet were recruited. Three different types of prefabricated insoles were tested: a control insole as an experimental control, a flat insole with only cushion pads for the shock absorbing function, and an arch support insole with both cushioning pads and arch support functions. Gait parameters and visual analog scale (VAS) scores for foot comfort were measured during 30 m of straight walking with each insole incorporating the wearable sensor device. The differences in gait parameters and foot comfort between the flat and arch support insoles relative to the control insole were analyzed. Additionally, the correlations between gait parameters and foot comfort were evaluated. Maximum plantarflexion angle significantly decreased (p = 0.03) and the toe-out angle significantly increased (p < 0.01) with arch support insoles compared to flat insoles. Significantly better foot comfort was demonstrated when walking with arch support insoles than with flat insoles (p < 0.01). The only gait parameter correlated with foot comfort was foot lift height while walking with an arch support insole (r = −0.45, p < 0.01). A novel smart insole revealed that foot lift height was a key gait parameter for determining foot comfort while walking with an arch support insole. Our findings provide important evidence for selecting a comfortable flatfoot insole based on gait data measured using a smart insole. • This study is the first smart insole-based gait analysis of insoles in patients with flatfoot. • Our novel smart insole is an effective tool to evaluate the effects of insoles. • Foot lift height was a key parameter for foot comfort while walking with an arch support insole. [ABSTRACT FROM AUTHOR]

In the process of the upper limb rehabilitation, the rehabilitation effect is often evaluated from the perspective of the motor function of limbs. However, the state of muscle atrophy is also a noteworthy indicator reflecting the rehabilitation effect. We proposed a wearable solution for the monitoring and grade assessing of local muscle atrophy based on wearable bioimpedance (BioZ) sensors. This work elaborates on the theoretical basis, procedure, and key influencing factors of the proposed solution, and the feasibility and effectiveness have been verified through in vitro and in vivo experiments. A total of 25 phantoms in different CSA (cross-sectional area) and FMR (fat-to-muscle ratio) values were designed to simulate different stages of muscular atrophy, and a linear correlation was observed between BioZ, CSA, and FMR, with an R-squared value of 0.898. The relative impedance difference of 38 patients with unilateral muscle atrophy was 5.231% larger than that of 30 healthy control samples on average (p < 0.05). These results demonstrate the correlation between muscle atrophy and BioZ. As the proof-of-concept for graded assessment, the results analyzed by support vector machines (SVMs) show that the accuracy of three-level classification can reach 94.1% using the five-fold cross-validation. [ABSTRACT FROM AUTHOR]

Exoskeleton robots have the potential to augment human motor capabilities. however, current control strategies often require task-specific control laws tailored for different scenarios, which limits the applicability of exoskeletons. In this study, we propose a control strategy for exoskeleton robots that is adaptable across various scenarios. We employ adaptive oscillators (AO) with feedback control to rapidly estimate the wearer'smotion phase and subsequently provide torque assistance to the wearer's hip joint based on a TCN-LSTM model. During experiments, we collected surface electromyographic (sEMG) signals from the tibialis anterior, gastrocnemius, and rectus muscles of seven groups of subjects performing treadmill walking and inclined treadmill exercises. We utilized the short-time Fourier transform to extract frequency characteristics of the signals and statistically analyzed the rate of frequency change in each muscle group under different strategies. The results indicate that when wearing the exoskeleton, the overall muscle frequency changes more slowly, suggesting that subjects can maintain activity for a longer duration before fatigue sets in. This control strategy effectively reduces the energetic cost of lower limb work for the wearer and enhances the exoskeleton's versatility in various applications. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia and osteoarthritis are prevalent age-related diseases that mutually exacerbate each other, creating a vicious cycle that worsens both conditions. Exercise is key to breaking this detrimental cycle. Facing increasing demand for rehabilitation services within this patient demographic, ChatGPT-4 and wearable device may increase the availability, efficiency and personalization of such health care. Aim: To evaluate the clinical efficacy and cost-effectiveness of a rehabilitation system implemented on mobile platforms, utilizing the integration of ChatGPT-4 and wearable devices. Methods: The study design is a prospective randomized open blinded end-point (PROBE) non-inferiority trial. 278 patients diagnosed with osteoarthritis and sarcopenia will be recruited and randomly assigned to the intervention group and the control group. In the intervention group patients receive mobile phone-based rehabilitation service where ChatGPT-4 generates personalized exercise therapy, and wearable device guides and monitor the patient to implement the exercise therapy. Traditional clinic based face-to-face exercise therapy will be prescribed and implemented in the control group. All patients will receive three-months exercise therapies following the frequency, intensity, type, time, volume and progression (FITT-VP) principle. The patients will be assessed at baseline, one month, three months, and six months after initiation. Outcome measures will include ROM, gait patterns, Visual Analogue Scale (VAS) for pain assessment, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS) for functional assessment, Short-Form Health Survey 12 (SF-12) for quality of life, Minimal Clinically Important Difference (MCID), Patient Acceptable Symptom State (PASS), and Substantial Clinical Benefit (SCB) for clinically significant measures. Discussion: A rehabilitation system combining the capabilities of ChatGPT-4 and wearable devices potentially enhance the availability and efficiency of professional rehabilitation services, thus enhancing the therapeutic outcomes for a substantial population concurrently afflicted with sarcopenia and osteoarthritis. [ABSTRACT FROM AUTHOR]

Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device's functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device's functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching 92.3 ± 2.6 % on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands. [ABSTRACT FROM AUTHOR]

Recently, eutectogels have emerged as ideal candidates for flexible wearable strain sensors. However, the development of eutectogels with robust mechanical strength, high stretchability, excellent transparency, and desirable conductivity remains a challenge. Herein, a covalently cross-linked eutectogel was prepared by exploiting the high solubility of oligoethylene glycol in a polymerizable deep eutectic solvent (DES) form of acrylic acid (AA) and choline chloride (ChCl). The resulting eutectogel exhibited high transparency (90%), robust mechanical strength (up to 1.5 MPa), high stretchability (up to 962%), and desirable ionic conductivity (up to 1.22 mS cm−1). The resistive strain sensor fabricated from the eutectogel exhibits desirable linear sensitivity (GF: 1.66), wide response range (1–200%), and reliable stability (over 1000 cycles), enabling accurate monitoring of human motions (fingers, wrists, and footsteps). We believe that our DES-based eutectogel has great potential for applications in wearable strain sensors with high sensitivity and reliability. [ABSTRACT FROM AUTHOR]

Fiber‐based thermoelectric (TE) device that can collect waste heat of human body and convert it into electricity is growing fast and is of great significance for sustainable development. Although ionic thermoelectric (i‐TE) ionogels are considered to be the next generation of TE materials, to construct ionogel fiber with synergistically enhanced mechanical and i‐TE performances remains big challenge. Herein, the poly(vinylidene fluoride‐co‐hexafluoropropylene)/1‐ethyl‐3‐methylimidazolium dicyanamide/ethanol/NaTFSI/SiO2 (PH/ED‐E‐Na‐SiO2) ionogel with ultrahigh ionic Seebeck coefficient of 47.5 mV K−1 and ionic conductivity of 43.7 mS cm−1 is constructed by a proposed antisolvent‐doping‐hybrid synergistic strategy. The enhanced i‐TE performances are mainly attributed to the low crystalline structure and the improvement in ions dissociation, migration, and diffusion difference. The corresponding power factor of the PH/ED‐E‐Na‐SiO2 ionogel film is as high as 9845.8 µW m−1 K−2, which is almost the highest value ever reported. Based on this, the ionogel fiber with comparable i‐TE performances and significantly enhanced tensile strength and strain (from 327 KPa and 47% of film to 17.7 MPa and 70% of fiber) is prepared by a simple coating process. It can be weaved into wearable i‐TE device that generates high thermovoltage and lights the light‐emitting diode (LED) bulb, demonstrating its great application potential as wearable energy supply device. [ABSTRACT FROM AUTHOR]

This study introduces a novel wearable device that combines dual‐functional modules for energy storage and sensing. The device features an ionic gel serving as both the battery electrolyte and sensor dielectric layer, along with VO2‐nanoneedles electrodes. The solid‐state zinc‐ion battery module demonstrates a specific capacity of 286 mAh g−1, delivering consistent and long‐lasting power output even under variable pressure conditions. Moreover, the iontronic pressure sensor module exhibits high sensitivity, achieving 2.3 × 104 kPa−1, enabling precise detection of fingertip pulses and identification of respiratory patterns. The device's flexibility and structural resilience allow it to endure complex deformations, ensuring continuous operation in challenging environments. These results highlight the potential of the all‐in‐one device for wearable smart healthcare monitoring and management. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusions\nIMPLICATIONS FOR REHABILITATIONTo evaluate the validity of a consumer-grade wearable for estimating energy expenditure, sedentary behaviour, and physical activity in manual wheelchair users with spinal cord injury (SCI).Fifteen manual wheelchair users with SCI (C5-L1, four female) completed activities of daily living and wheelchair propulsion (2–8 km·h−1). Wrist-worn accelerometry data were collected using consumer-grade (z-Track) and research-grade (ActiGraph GT9X) devices. Energy expenditure was measured via indirect calorimetry. Linear regression was used to evaluate the prediction of criterion metabolic equivalent of task (MET) by each accelerometer’s vector magnitude (VM). Area under the receiver operating characteristic curve (ROC-AUC) evaluated the accuracy of VM for discriminating between physical activity intensities and for identifying accelerometer cut-points.Standardised β-coefficients for the association between z-Track and ActiGraph VM for criterion MET were 0.791 (p < 0.001) and 0.774 (p < 0.001), respectively. The z-Track had excellent accuracy for classifying time in sedentary behaviour (ROC-AUC = 0.95) and moderate-to-vigorous physical activity (ROC-AUC = 0.93); similar values to the ActiGraph (ROC-AUC = 0.96 and 0.88, respectively). Cut-points for the z-Track were ≤37 g·min−1 for sedentary behaviour and ≥222 g·min−1 for moderate-to-vigorous physical activity.This study supports the validity of a consumer-grade wearable to measure sedentary time and physical activity in manual wheelchair users with SCI.A consumer-grade wearable device provides valid estimations of sedentary time and physical activity in manual wheelchair users with spinal cord injury.Commercially available consumer-grade wearables may enable accurate self-monitoring in this population and, therefore, have potential for supporting behaviour change.A consumer-grade wearable device provides valid estimations of sedentary time and physical activity in manual wheelchair users with spinal cord injury.Commercially available consumer-grade wearables may enable accurate self-monitoring in this population and, therefore, have potential for supporting behaviour change. [ABSTRACT FROM AUTHOR]

Background: As walking abilities are widely affected among the aging population, investigating the effectiveness of wearable device-based walking programs is essential. The intentions of this meta-analysis were to investigate their effects on gait speed among older adults, as well as to include subgroup analysis to evaluate potential effects on individuals with aging-related conditions such as Parkinson's disease (PD) and stroke. Methods: Systematic retrieval of Pubmed, The Cochrane Library, Embase and Web of Science databases were searched up to February 2024. Outcomes such as gait speed, balance, cadence, and stride length were extracted and analyzed. Study quality was evaluated using the Rob 2 tool and heterogeneity was tested using I2 statistics through STATA 16. Results: Nine studies with 284 participants were analyzed. The intervention group showed a significant improvement in gait speed (weighted mean difference (WMD) 0.12; 95% CI 0.03 to 0.21). There is a subgroup analysis suggesting differential effects: significant improvements in PD and stroke subgroups, but not in the normal aging group. Balance (WMD: 1.93; 95% CI: 0.20 to 3.66) and stride length (WMD: 8.58; 95% CI: 3.04 to 14.12) were also shown to improve, but the heterogeneity across the studies was moderate (I2 = 63.91%). No significant changes were observed in the Timed Up and Go test, Gait Variability, and Step Width. Conclusions: Wearable device-based walking programs improve gait speed in older adults, with top notch advantages in the ones tormented by PD or stroke. These findings advocate that such interventions can be a valuable part of individualized treatment strategies in geriatric care, aiming to enhance mobility and usual satisfactory of existence. [ABSTRACT FROM AUTHOR]

Introduction: This study examined how a six-week progressive flywheel resistance training program for the core muscles affects gymnasts' core explosive strength and floor exercise performance. Methods: Ten elite male artistic gymnasts were recruited. Core explosive strength was measured using average power, maximum concentric power, maximum eccentric power, and average strength obtained from a flywheel resistance training machine. Inertial sensors were used to collect trunk acceleration and rotational angular velocity during straight-back somersaults with 2/1 and 3/1. After the pretest, the participants were divided into experimental and control groups (n = 5/group) using a randomized crossover trial design. Following six weeks of progressive training, a posttest was conducted to collect relevant data. After a seven-week washout period, the two groups were swapped and underwent another six weeks of training. Statistical analyses were performed using dependent and independent samples t-tests, considering a significance level of α = 0.05. Results: The experimental group showed significant pretest-to-posttest improvements in average power and maximum concentric power during the supine rotational explosive strength tests and in average power, maximum eccentric power, and average strength during prone rotational explosive strength tests (p < 0.05). It also showed significant pretest-to-posttest improvements in trunk acceleration at takeoff in straight-back somersaults with 2/1 and 3/1 (p < 0.05). Conclusion: The adopted training program can effectively enhance the core explosive strength and trunk acceleration capabilities of gymnasts' floor exercise athletic performance. Future studies may integrate lower body and core training to explore the optimal training schedules for artistic gymnasts. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) represents a frequent cause of hospital admission, with fluid overload directly contributing to decompensations. Bioimpedance (BI), a physical parameter linked to tissue hydration status, holds promise in monitoring congestion and improving prognosis. This systematic review aimed to assess the clinical relevance of BI-based wearable devices for HF fluid monitoring. Methods: A systematic review of the published literature was conducted in five medical databases (PubMed, Scopus, Cochrane, Web of Science, and Embase) for studies assessing wearable BI-measuring devices on HF patients following PRISMA recommendations on February 4th, 2024. The risk of bias was evaluated using the ROBINS tool. Results: The review included 10 articles with 535 participants (mean age 66.7 ± 8.9 years, males 70.4%). Three articles identified significant BI value differences between HF patients and controls or congestive vs non-congestive HF patients. Four articles focused on the devices’ ability to predict HF worsening-related events, revealing an overall sensitivity of 70.0 (95% CI 68.8–71.1) and specificity of 89.1 (95% CI 88.3–89.9). One article assessed prognosis, showing that R80kHz decrease was related to all-cause-mortality with a hazard ratio (HR) of 5.51 (95% CI 1.55–23.32; p = 0.02) and the composite all-causemortality and HF admission with a HR of 4.96 (95% CI 1.82–14.37; p = 0.01). Conclusions: BI-measuring wearable devices exhibit efficacy in detecting fluid overload and hold promise for HF monitoring. However, further studies and technological improvements are required to optimize their impact on prognosis compared to standard care before they can be routinely implemented in clinical practice. [ABSTRACT FROM AUTHOR]

Polypropylene carbonate (PPC) is a promising candidate of substrate materials used for human wearable devices due to its excellent ductility and biocompatibility. However, PPC is prone to be deformed, thus resulting in poor shape recovery property. One effective solution is to construct composites. In this work, poly (tannic acid) with Fe3+ (FPTA) @ graphene oxide (GO) was added to fabricate FPTA@GO/PPC composites. The results show that FPTA@GO can construct a tight network of hydrogen bonds into PPC to improve shape memory and self‐healing properties. By varying the FPTA@GO content, the composites exhibit tunable shape memory property. Dynamic mechanical analysis confirms that FPTA@GO/PPC composites show better shape memory properties compared with pure PPC. 5 wt% FPTA@GO/PPC composite demonstrates superior shape fixation ratio (Rf = 99%) as well as superior shape recovery ratio (Rr = 94%) in 37°C. Moreover, 5 wt% FPTA@GO/PPC composite possesses great self‐healing efficiency of 81% and tensile strength (17.2 MPa). The UV absorption capacity of 5 wt% FPTA@GO/PPC is increased by 150% (far and mid‐UV regions) and 400% (near‐UV region) compared with pure PPC. Thus, FPTA@GO/PPC composites have potential applications in developing shape memory, self‐healing, and UV‐resistant materials for human wearable devices. [ABSTRACT FROM AUTHOR]

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects. [ABSTRACT FROM AUTHOR]

Background and aims: Electrical stimulation (ES) has been shown to substantially enhance the quality of life by alleviating pain in patients with chronic wounds. This study aimed to observe the effects of low‐frequency pulsed wearable ES at the common peroneal nerve on chronic refractory wounds of the lower limb. Methods: Forty‐eight participants were randomly divided into control group (n = 24) and treatment group (n = 24) in this study. The control group received standard wound care (SWC) exclusively, whereas the treatment group was administered both SWC and the wearable low‐frequency ES targeting the common peroneal nerve. Measurements of wound area, pain intensity, wound status, and quality of life scores were systematically recorded both before and after 4 weeks treatment. Results: After 4 weeks of intervention, the percentage area reduction was significantly higher in the treatment group compared to the control group (Z = −3.9, p < 0.001), and the healing rate of the treatment group was significantly higher than that of the control group (33% vs. 4%). Moreover, the Visual Analog Scale for Pain score (β = −0.65, p = 0.019), the Bates‐Jensen Wound Assessment Tool score (p < 0.05), and the questionnaire on quality of life with chronic wounds (Wound‐Qol) score (β = −4.23, p = 0.003) were significantly decreased in the patients in the treatment group compared to the control group. Conclusion: The wearable low‐frequency pulsed ES at the common peroneal nerve for the treatment of chronic refractory wounds showed significant improvement and were far superior compared to SWC. Future research should broaden its scope to include a diverse range of wound types and benefit from collaboration across multiple research centers. Key messages: Traditional wound dressings are currently inadequate for addressing chronic wound closure.The wearable electrical stimulation (ES), an economical and noninvasive form of physical therapy, can be beneficial in facilitating the healing process of chronic wounds.The purpose of this study was to observe the effects of wearable low‐frequency pulsed ES at the common peroneal nerve on chronic refractory wounds of the lower limb.The wearable low‐frequency pulsed ES at the common peroneal nerve for the treatment of chronic refractory wounds showed significant improvement and were far superior compared to standard wound care. [ABSTRACT FROM AUTHOR]

To diagnose diabetes early or to maintain stable blood glucose levels in diabetics, blood glucose levels should be frequently checked. However, the only way to check blood glucose levels regularly is to use invasive methods, such as pricking the fingertip or using a minimally invasive patch. These invasive methods pose several problems, including being painful and potentially causing secondary infections. This study focuses on noninvasively measuring glycated hemoglobin (HbA1c) using PPG signals. In particular, the study relates to a method and a hardware design technology for removing noise that may be present in a PPG signal due to skin contact with a noninvasive HbA1c measurement device. The proposed HbA1c measurement device consists of the first sensor (PPG sensor) module including an optical barrier and the second sensor (cylindrical sensor) module for removing the skin effect. We have developed a Monte Carlo method to implement accurate, noninvasive HbA1c measurement by considering different skin properties among different subjects. Implementing this model in wearable devices will allow end users to not only monitor their glycated hemoglobin levels but also control diabetes with higher accuracy without needing any blood samples. This will be a groundbreaking advancement in modern wearable medical devices. [ABSTRACT FROM AUTHOR]

Abstract Background Sarcopenia and osteoarthritis are prevalent age-related diseases that mutually exacerbate each other, creating a vicious cycle that worsens both conditions. Exercise is key to breaking this detrimental cycle. Facing increasing demand for rehabilitation services within this patient demographic, ChatGPT-4 and wearable device may increase the availability, efficiency and personalization of such health care. Aim To evaluate the clinical efficacy and cost-effectiveness of a rehabilitation system implemented on mobile platforms, utilizing the integration of ChatGPT-4 and wearable devices. Methods The study design is a prospective randomized open blinded end-point (PROBE) non-inferiority trial. 278 patients diagnosed with osteoarthritis and sarcopenia will be recruited and randomly assigned to the intervention group and the control group. In the intervention group patients receive mobile phone-based rehabilitation service where ChatGPT-4 generates personalized exercise therapy, and wearable device guides and monitor the patient to implement the exercise therapy. Traditional clinic based face-to-face exercise therapy will be prescribed and implemented in the control group. All patients will receive three-months exercise therapies following the frequency, intensity, type, time, volume and progression (FITT-VP) principle. The patients will be assessed at baseline, one month, three months, and six months after initiation. Outcome measures will include ROM, gait patterns, Visual Analogue Scale (VAS) for pain assessment, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS) for functional assessment, Short-Form Health Survey 12 (SF-12) for quality of life, Minimal Clinically Important Difference (MCID), Patient Acceptable Symptom State (PASS), and Substantial Clinical Benefit (SCB) for clinically significant measures. Discussion A rehabilitation system combining the capabilities of ChatGPT-4 and wearable devices potentially enhance the availability and efficiency of professional rehabilitation services, thus enhancing the therapeutic outcomes for a substantial population concurrently afflicted with sarcopenia and osteoarthritis.

[Display omitted] • Achieve high-precision DIW printing using conductive hydrogel ink. • Conductive hydrogel electrodes have remarkable stretchability and self-healing capabilities. • Develop a self-powered strain sensing system employing conductive hydrogel electrodes. • Construct a complex internal structure within the electrode. • Remote human motion detection based on hydrogel strain sensors. With rapid advancements in health and human–computer interaction, wearable electronic skins (e-skins) designed for application on the human body provide a platform for real-time detection of physiological signals. Wearable strain sensors, integral functional units within e-skins, can be integrated with Internet of Things (IoT) technology to broaden the applications for human body monitoring. A significant challenge lies in the reliance of most existing wearable strain sensors on rigid external power supplies, limiting their practical flexibility. In this study, we present an innovative strategy to fabricate glutaraldehyde (GA)-poly(vinyl alcohol) (PVA)/cellulose nanocrystals (CNC)/Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) conductive hydrogels through multiple hydrogen bonding systems. Combining the advantageous rheological properties of the precursor solution and the high specific surface area after freeze–thaw cycling, we have created a self-powered sensing system prepared by large-area printing using direct ink writing (DIW) printing. The resulting conductive hydrogel exhibits commendable mechanical properties (411 KPa), impressive stretchability (580 %), and robust self-healing capabilities (>98.3 %). The strain sensor, derived from the conductive hydrogel, demonstrates a gauge factor (GF) of 2.5 within a stretching range of 0–580 %. Additionally, the resultant supercapacitor displays a peak energy density of 0.131 mWh/cm3 at a power density of 3.6 mW/cm3. Benefiting from its elevated strain response and remarkable power density features, this self-powered strain sensing system enables the real-time monitoring of human joint motion. The incorporation of a 5G transmission module enhances its capabilities for remote data monitoring, thereby contributing to the progress of wireless tracking technologies for self-powered electronic skin. [ABSTRACT FROM AUTHOR]

[Display omitted] • MOF nanosheets reconfigured by electrostatic coupling and interfacial conditioning strategies. • PEDOT:PSS and MOF nanosheets with interoperable conductive network and excellent interfacial properties. • MOF nanosheets attenuate the entanglement of PEDOT:PSS. • Homogeneous ink prepared using solution engineering for flexible supercapacitor preparation. Two-dimensional metal – organic frameworks (2D MOFs) hold great promise as electrochemically active materials. However, their application in MOF nanocomposite electrodes in solution engineering is limited by structural self-stacking and imperfect conductive pathways. In this study, we used meso -tetra(4-carboxyphenyl) porphine (TCPP) with off-domain π-bonds to reconstitute Zn-TCPP (ZMOF) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) through an interfacial modulation strategy involving electrostatic coupling and hydrogen bonding, creating a conductive composite with a nanosheet structure. The negatively charged PSS and ZMOF formed a three-dimensional interconnected conductive network with excellent interfaces. The positively charged PEDOT, fine tuned with the lamellar structure, established strong π-π stacking interactions between the porphyrin and thiophene rings. ZMOF also induced changes in the PEDOT chain structure, weakening PSS entanglement and enhancing charge-transport properties. The specific capacitance of the prepared supercapacitor was as high as 967.8 F g−1. Flexible supercapacitors produced on a large scale using dispensing printing technology exhibited an energy density of 1.85 μWh cm−2 and a power density of 7.08 μW cm−2. This interfacial modulation strategy also exhibited excellent wearable properties, with 96 % capacitance retention at a 180° bending angle and stable cycling performance. This study presented a significant advancement in the functionalization of 2D materials, highlighting their potential for device-grade capacitive architectures. [ABSTRACT FROM AUTHOR]

Maximilian Boesch,1 Florent Baty,1 Stefan Bilz,2 Martin H Brutsche,1 Frank Rassouli1 1Lung Center, Cantonal Hospital St.Gallen, St.Gallen, Switzerland; 2Division of Endocrinology & Diabetes, Cantonal Hospital St.Gallen, St.Gallen, SwitzerlandCorrespondence: Maximilian Boesch, Ph.D., Lung Center, Cantonal Hospital St. Gallen, Rorschacherstrasse 95, St. Gallen, CH-9007, Switzerland, Tel +41-71-494-7143, Email maximilian.boesch@kssg.chIntroduction: Lung function constraints and comorbidities such as coronary heart disease, sarcopenia, and mood disorders make chronic obstructive pulmonary disease (COPD) patients avoid physical activity (PA). However, PA represents an important pillar of COPD management and is explicitly recommended by professional associations to enhance physical functioning and positively modulate disease progression.Methods: In this monocentric, prospective, observational feasibility study, it was our primary objective to investigate the association between PA and the evolution of the COPD assessment test (CAT) and the occurrence of acute exacerbations of COPD (AECOPD), respectively. To this end, we equipped 42 COPD patients with an activity tracking wearable and telemonitored their daily PA levels over one year using a dedicated web-based interface. Patients additionally provided weekly CAT scores using the same telehealth platform and came in for 3 study visits to assess functional parameters and biochemical markers related to nutrition and inflammation.Results: A principal study finding was that PA was inversely associated with CAT score (drop of 0.21 points associated with an increase of 1000 daily steps, p = 0.004), and that the 50% of patients with higher PA levels showed less CAT score progression over time (0.42 points per year) than the 50% of patients with lower PA levels (3.26 points per year) (p < 0.001). In addition, higher PA levels were significantly associated with a lower likelihood of experiencing a moderate-to-severe AECOPD (31% risk reduction associated with an increase of 1000 daily steps, p = 0.0097).Discussion: Our study demonstrates the relevance of PA for key COPD outcome metrics in a real-world setting and underpins the importance of PA for COPD self-management in everyday life. Our study paves the way for future intervention trials to prospectively identify medically relevant PA thresholds and establish training recommendations for different patient subgroups.Keywords: CAT score, COPD, physical activity, telemonitoring, wearable device

Wearable devices have good portability and imperceptibility， realizing expected functions after being worn. Drug delivery refers to delivering drugs into the body with suitable carriers or techniques to generate therapeutic effects to improve the stability and bioavailability of drugs.The application of wearable devices in drug delivery can rationally release drugs when receiving disease signals or user orders and monitor in-vivo drug concentrations， reducing the dependency of doctors or hospitals， and achieving optimal disease treatment timing and outcomes. Wearable drug delivery systems can be worn directly on the body surface and are non-invasive and self-administered. Microneedle-skin patches， wound healing patches， and smart contact lenses are popular wearable devices explored in drug delivery. This review is a summary of the application of wearable devices in disease treatment， such as diabetes， wound healing， eye diseases， cancer， and other diseases， and drug delivery in recent five years， summarizing the challenges for the development of wearable drug delivery systems， and prospecting its development direction.

Abstract Background The long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions. Methods Fourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height. Results C2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p

Abstract Background Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke. Methods Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl–Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers’ active range of motion (AROM) and Dunnett’s test was used for pairwise comparisons. Results Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p

Cardiovascular disease is the leading cause of death globally. This research implements a system for monitoring heart rate, electrocardiogram, and providing alerts for potential risks to patients based on data collected using the LSTM machine learning model. The wearable device is compact in size with a long battery life. The information collected from the device can be remotely monitored by doctors through a visual interface on a web server model, and patients can self-monitor their electrocardiogram status through an application on a mobile device. By integrating the LSTM model into the design, this study has addressed two issues: predicting the trend of the electrocardiogram signal and detecting abnormalities in the predicted signal. This allows users to self-monitor their personal status and doctors to better adjust the treatment method for the patient’s health.

Abstract Introduction People with Parkinson’s Disease (PD) show abnormal gait patterns compromising their independence and quality of life. Among all gait alterations due to PD, reduced step length, increased cadence, and decreased ground-reaction force during the loading response and push-off phases are the most common. Wearable biofeedback technologies offer the possibility to provide correlated single or multi-modal stimuli associated with specific gait events or gait performance, hence promoting subjects’ awareness of their gait disturbances. Moreover, the portability and applicability in clinical and home settings for gait rehabilitation increase the efficiency in the management of PD. The Wearable Vibrotactile Bidirectional Interface (BI) is a biofeedback device designed to extract gait features in real-time and deliver a customized vibrotactile stimulus at the waist of PD subjects synchronously with specific gait phases. The aims of this study were to measure the effect of the BI on gait parameters usually compromised by the typical bradykinetic gait and to assess its usability and safety in clinical practice. Methods In this case series, seven subjects (age: 70.4 ± 8.1 years; H&Y: 2.7 ± 0.3) used the BI and performed a test on a 10-meter walkway (10mWT) and a two-minute walk test (2MWT) as pre-training (Pre-trn) and post-training (Post-trn) assessments. Gait tests were executed in random order with (Bf) and without (No-Bf) the activation of the biofeedback stimulus. All subjects performed three training sessions of 40 min to familiarize themselves with the BI during walking activities. A descriptive analysis of gait parameters (i.e., gait speed, step length, cadence, walking distance, double-support phase) was carried out. The 2-sided Wilcoxon sign-test was used to assess differences between Bf and No-Bf assessments (p

Today age of advancement one of the fastest growing fields of the technology is wearable electronics and device. In the recent advancement the wearable devices for on and off body communication is going expeditiously. For the wearable wireless communication, wearable antennas are mostly used due to its compact size, self powered, light weight, low profile, portable wireless communication and sensing. This paper throws light on wearable antennas for on body and off body communication including their applications, advantages and disadvantages. A comparative study is conducted on designing of different on body and off body wearable antennas and parameters of designed antenna such as their size, shape, gain, SAR have been compared and analyzed. In this paper also discussed the impact of the wearable antenna on human body and impact of human body on antenna.

Wearable technology has seen rapid advancement, yet the integration of responsive materials into wearable devices poses significant challenges, particularly in maintaining fabric integrity and user comfort while ensuring sensitivity and responsiveness to environmental stimuli. In this work, these challenges are addressed by developing an ultra‐stable hydrochromic fabric that exhibits both hydro‐ and halochromic responsiveness. Utilizing a bimolecular fluoran dye system composed of a black leuco dye (ODB‐2) and a weak acid developer (benzyl 4‐hydroxybenzoate, B4H), these materials are embedded into a robust fibrous matrix constructed through an electrospinning process with thermoplastic polyurethane (TPU). This approach ensures the breathability, flexibility, and structural integrity of the fabrics, while the hydrophobic nature of TPU contributes to the stability and reversibility of the hydro/halochromic properties. The strategy allows for immediate, high‐contrast color changes upon exposure to water and acidic/basic vapors. These fabrics are also applied in rewritable data encryption, demonstrating their potential in anti‐counterfeiting. Furthermore, the investigation into the mechanical properties of these fabrics confirms their durability and resilience, making them ideal for wearable technology. [ABSTRACT FROM AUTHOR]

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response. [ABSTRACT FROM AUTHOR]

The wearable device market is growing rapidly, as evidenced by the growth in sales of smartwatches. However, these have a tiny touch screen so users cannot perform complex operations with a smartwatch. Few studies address the user experience of gestures to interact with wearable devices. This study uses a bone-conduction smartwatch to study the user experience of gestures for interaction with a wearable device in terms of four dimensions: pragmatic quality, hedonic quality–identification and hedonic quality–stimulation and attractiveness. Task loads ae also evaluated for further analysis of the user experience This study determines the relationship between these factors to determine the effect of using hand gestures to interact with wearable devices. The results of this study show that all of the users easily accept gesture interaction with wearable devices. Pragmatic quality, hedonic quality–identification and hedonic quality–stimulation are positively associated with attractiveness. A higher level of attractiveness reduces the perceived task load. Attractiveness has a significant positive effect on performance and task load has a significant negative effect on performance. [ABSTRACT FROM AUTHOR]

TheraBracelet is a peripheral vibrotactile stimulation applied to affected upper extremities via a wristwatch-like wearable device during daily activities and therapy to improve upper limb function. The objective of this study was to examine the feasibility of using TheraBracelet for a child with hemiplegic cerebral palsy. Methods: A nine-year-old male with cerebral palsy was provided with TheraBracelet to use during daily activities in the home and community settings for 1.5 years while receiving standard care physical/occupational therapy. Results: The child used TheraBracelet independently and consistently, except during summer vacations and elbow-to-wrist orthotic use from growth spurt-related contracture. The use of TheraBracelet did not impede or prevent participation in daily activities. No study-related adverse events were reported by the therapist, child, or parent. Future research is warranted to investigate TheraBracelet as a propitious therapeutic device with a focus on the potential impact of use to improve the affected upper limb function in daily activities in children with hemiplegic cerebral palsy. [ABSTRACT FROM AUTHOR]

Background: Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke. Methods: Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl–Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers' active range of motion (AROM) and Dunnett's test was used for pairwise comparisons. Results: Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p < 0.001) during MR-board 2 training and were maintained until follow-up. All AROM values of the finger joints changed significantly during training (p < 0.001). Conclusions: MR-board 2 self-training, which includes natural interactions between humans and computers using a tangible user interface and real-time tracking of the fingers, improved upper limb function across impairment, activity, and participation. MR-board 2 could be used as a self-training tool for patients with stroke, improving their quality of life. Trial registration number: This study was registered with the Clinical Research Information Service (CRIS: KCT0004167). [ABSTRACT FROM AUTHOR]

Background: The long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions. Methods: Fourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height. Results: C2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = −0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = −0.388, p < 0.001). The internal and external training load variables were similar among different playing positions. Conclusion: The participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height. [ABSTRACT FROM AUTHOR]

Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET. [ABSTRACT FROM AUTHOR]

Accurately obtaining a patient's respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from body movements. Conversely, a capnometer coupled with a ventilator offers a method of measuring the respiratory rate that is unaffected by body movements. However, capnometers are mainly used to evaluate respiration when using a ventilator or an Ambu bag by measuring the CO2 concentration at the breathing circuit, and they are not used only to measure the respiratory rate. Furthermore, capnometers are not suitable as wearable devices because they require intubation or a mask that covers the nose and mouth to prevent air leaks during the measurement. In this study, we developed a reliable system for measuring the respiratory rate utilizing a small wearable MOx sensor that is unaffected by body movements and not connected to the breathing circuit. Subsequently, we conducted experimental assessments to gauge the accuracy of the rate estimation achieved by the system. In order to avoid the effects of abnormal states on the estimation accuracy, we also evaluated the classification performance for distinguishing between normal and abnormal respiration using a one-class SVM-based approach. The developed system achieved 80% for both true positive and true negative rates. Our experimental findings reveal that the respiratory rate can be precisely determined without being influenced by body movements. [ABSTRACT FROM AUTHOR]

Background/Aims: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. Methods: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov—a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. Results: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. Conclusions: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The prevalence of e‐cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de‐noise beat‐to‐beat interval (BBI) data from smartwatches worn by 12 young adult regular e‐cigarette users for 7 days; and (2) summarize the de‐noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de‐noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention. [ABSTRACT FROM AUTHOR]

Fewer layer graphene principally has a higher surface area to support more catalytic sites for energy conversion, but it is still challenging to synthesize monolayer graphene without oxidation from graphite at a low cost. Here, a method for synergistically thinning graphene layers and constructing catalytic sites to create a superior bifunctional oxygen catalyst through vapor intercalation of multi‐layer graphene or its derivatives is described. The synthesized small sheet sizes of 1–2 layer graphene‐supported FeN4 and FeCo active sites exhibit superior reversible activity of oxygen reduction and evolution reactions with a low overall overpotential of 0.648 V. The sheet‐shaped catalyst is further used to fabricate flexible soft‐packed and wearable cable‐type quasi‐solid‐state zinc–air batteries, achieving high performances (>188 mW cm−2, >450 cycles) and enabling smartphone charging. DFT calculations reveal that fewer layer graphene coupled with atomic FeN4 and alloy FeCo sites enables lower Gibbs free energy for favorable OOH* intermediate adsorption/desorption benefiting superior oxygen redox process. This study introduces a strategy for scalable synthesis of 1–2 layer graphene from cheap microcrystalline graphite minerals for wearable and durable energy devices. [ABSTRACT FROM AUTHOR]

Self‐powered pressure detection using smart wearable devices is the subject of intense research attention, which is intended to address the critical need for prolonged and uninterrupted operations. Current piezoelectric and triboelectric sensors well respond to dynamic stimuli while overlooking static stimuli. This study proposes a dual‐response potentiometric pressure sensor that responds to both dynamic and static stimuli. The proposed sensor utilizes interdigital electrodes with MnO2/carbon/polyvinyl alcohol (PVA) as the cathode and conductive silver paste as the anode. The electrolyte layer incorporates a mixed hydrogel of PVA and phosphoric acid. The optimized interdigital electrodes and sandpaper‐like microstructured surface of the hydrogel electrolyte contribute to enhanced performance by facilitating an increased contact area between the electrolyte and electrodes. The sensor features an open‐circuit voltage of 0.927 V, a short‐circuit current of 6 µA, a higher sensitivity of 14 mV/kPa, and outstanding cycling performance (>5000 cycles). It can accurately recognize letter writing and enable capacitor charging and LED lighting. Additionally, a data acquisition and display system employing the proposed sensor, which facilitates the monitoring of athletes' rehabilitation training, and machine learning algorithms that effectively guide rehabilitation actions are presented. This study offers novel solutions for the future development of smart wearable devices. [ABSTRACT FROM AUTHOR]

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa−1 from 0 to 30 kPa) and wide working pressure range (0–200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human–machine interaction. [ABSTRACT FROM AUTHOR]

A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models. [ABSTRACT FROM AUTHOR]

Introduction: Widespread use of smartphone applications has opened new perspectives for home Blood Pressure monitoring based on mobile health (mHealth) technologies. Patient engagement has been dubbed 'the silver bullet of the century'. Aim: The aim was to identify the impact of engagement in patients with blood pressure using mHealth. Methods: This scoping review was conducted in accordance with the Ark0sey and O'Malley framework. Database: Pubmed, CINAHL, Scopus and PsycInfo. This review considered both qualitative and quantitative primary searches. We excluded articles belonging to grey literature, secondary literature and paediatric setting. Between September and November 2023, the review was carried out. Results: A total of 569 documents were retrieved from the four databases. After the deduplication process, five articles were removed. The selection process based on titles and abstracts included 133 records. Ten studies were selected and analysed. The reviewers identified the following themes: device type and mobile applications, engagement, blood pressure control, health behaviours and hypertension knowledge. Self-management using digital technologies in the home is strongly linked to engagement, reduction and control of Blood Pressure, improved health practices and increased knowledge of hypertension. Healthcare interventions using IT platforms have had a significant impact on the health outcomes of patients diagnosed with hypertension. Conclusions: The review findings suggest the value of these technologies in improving patient engagement and, consequently, adherence to antihypertensive treatment and achieving blood pressure control rates, potentially reducing cardiovascular risk. [ABSTRACT FROM AUTHOR]

With the rapid development of novel smart electronics, there is an increasing demand for consumer electronic products that possess high performance, light weight and flexibility. However, traditional rigid silicon-based devices struggle to meet the specific requirements of fields such as wearable devices and electronic skins. Two-dimensional (2D) materials have garnered significant attention in the emerging electronics industry due to their excellent optoelectronic properties, making the flexible transparent 2D-based optoelectronic devices become a forefront research area in cutting-edge technology. This paper provides a detailed introduction to the key components of flexible transparent 2D optoelectronic devices, including 2D materials, transparent electrodes, and flexible transparent substrates/dielectric layers. It comprehensively discusses the wideranging applications of devices, such as flexible transparent transistors, photodetectors, sensors, and capacitors for wearable electronics, transparent smart displays, and medical monitoring. Finally, it summarizes the current challenges and development prospects of flexible transparent 2D optoelectronic devices. With the continuous innovation and development of materials science, nanotechnology, and manufacturing processes, it is believed that flexible transparent electronic technology will become more mature and reliable, bringing greater convenience and possibilities to human life. [ABSTRACT FROM AUTHOR]

Sudden cardiac arrest during exercise can occur without prior warning signs at rest, highlighting the importance of monitoring for its prevention. To detect the signs of ischemic heart disease, including coronary artery anomalies, ST changes must be detected using three‑lead electrocardiograms (ECGs) corresponding to each region of the three coronary artery branches. We conducted ECG monitoring of five runners during a marathon using a wearable three‑lead ECG device (e-skin ECG; Xenoma Inc., Tokyo, Japan). Data without noise or artifacts were successfully collected for one of five runners during the entire marathon. Within the initial hour of the marathon, poor electrode adhesion to the skin hindered the data collection for the remaining four runners, which resulted in significantly decreased acquisition rate compared with the first hour (86.7 ± 13.4 % to 37.3 ± 36.9 %, p = 0.028). Couplets of premature ventricular contractions with clear ECG waveforms in the three leads were detected in one runner during the marathon. Further device improvements are necessary to enable marathon runners to obtain ECGs efficiently without affecting their performance. This study also demonstrated the potential applications of three‑lead wearable ECG monitoring for other short-duration sports and remote home-based cardiac rehabilitation. This is an initial trial of a three‑lead wearable electrocardiogram (ECG) monitoring device during a full marathon. ECG data were obtained with low noise and artifacts during the first hour of the marathon; however, the data acquisition rate decreased in the middle and late stages owing to poor electrode adhesion. This study demonstrated the possibility of applying wearable ECG monitoring during short-term exercise and cardiac rehabilitation to detect warning signs and prevent sudden cardiac arrest. [ABSTRACT FROM AUTHOR]

• Home-based cardiac telerehabilitation based on wearable electrocardiogram or heart rate monitoring devices has a positive effect on cardiac function (left ventricular ejection fraction, peak oxygen intake, 6 min walking distance). • Long-term (≥3 months) cardiac rehabilitation might benefit individuals suffering from anxiety or depression. • The main monitoring devices used for home-based cardiac telerehabilitation are wearable electrocardiogram monitors, heart rate belts, and monitoring watches. To evaluate the effectiveness of home-based cardiac telerehabilitation based on wearable electrocardiogram or heart rate monitoring devices in patients with heart disease. We searched eight electronic databases under the guidance of Cochrane Handbook and PRISMA recommendations. The meta-analysis included data from 14 articles (15 RCTs) representing 1314 participants. A significant improvement in left ventricular ejection fraction [MD = 2.12, 95 % CI (1.21, 3.04), P < 0.001], 6-minute walk distance [MD = 40.00, 95 % CI (21.72, 58.29), P < 0.001] and peak oxygen intake [MD = 2.24, 95 % CI (1.38, 3.10), P < 0.001] were observed in the home-based cardiac telerehabilitation group. But it had no difference in anxiety [SMD = -0.83, 95 % CI (-1.65, -0.02), P = 0.05] and depression [SMD = -0.59, 95 % CI (-1.26, 0.09), P = 0.09]. Subgroup analyses revealed that interventions of no less than 3 months improved anxiety [SMD = -1.11, 95 % CI (-2.05, -0.18), P = 0.02] and depression [SMD = -1.01, 95 % CI (-1.93, -0.08), P = 0.03]. Home-based cardiac telerehabilitation based on wearable electrocardiogram or heart rate monitoring devices has a positive effect on cardiac function. Long-term (≥ 3 months) cardiac rehabilitation might benefit individuals suffering from anxiety or depression. [ABSTRACT FROM AUTHOR]