Wearable Devices Research Articles

High-Conductivity and Ultrastretchable Self-Healing Hydrogels for Flexible Zinc-Ion Batteries.

Aqueous zinc-ion batteries are promising candidates for flexible energy storage devices due to their safety, economic efficiency, and environmental friendliness. However, the uncontrollable dendrite growth and side reactions at the zinc anode hinder their commercial application. Herein, we designed and synthesized a dual network self-healing hydrogel electrolyte with zwitterionic groups (PAM-PAAS-QCS), which can be used for the large deformations of flexible devices due to its excellent stretchability (ε = 5100%). The incorporation of zwitterionic groups into the PAM-PAAS-QCS hydrogel electrolyte endows it with high ionic conductivity (33.61 mS/cm), a wide electrochemical stability window, and the ability to suppress zinc dendrite formation and side reactions. Besides, the Zn//Zn symmetric cell with PAM-PAAS-QCS can stably plate and strip zinc for 1500 h at 0.5 mA/cm2, and the Zn//Polyaniline full cell retains 82.4% of its capacity after 1500 cycles at 1 A/g. Additionally, flexible batteries based on both the original and self-healed PAM-PAAS-QCS hydrogel electrolytes demonstrate good cycling stability and stable charge-discharge performance under various bending conditions. This self-healing hydrogel electrolyte with excellent stretchability and high ionic conductivity is expected to pave the way for the development of high-performance flexible energy storage and wearable devices.

High-Performance Flexible Pressure Sensor with Micropyramid Structure for Human Motion Signal Detection and Electromagnetic Interference Shielding.

Flexible pressure sensors have a wide range of applications in the field of human motion signal detection, but the electromagnetic radiation generated during the monitoring process has become an unavoidable problem. Nowadays, it is still a challenge to develop high-performance pressure sensors with excellent electromagnetic interference (EMI) shielding performance. Herein, the Ti3C2TX MXene/carbon fiber/multiwalled carbon nanotube/polydimethylsiloxane (MXene/CMP) films with a micropyramidal structure were developed by the technology of vacuum high-temperature hot pressing and spray deposition. Benefiting from the dielectric loss of the conductive fillers and the presence of the microstructure, the MXene/CMP film exhibits excellent EMI shielding performance, and the EMI shielding efficiency (SE) can reach 49.37 dB. Besides, the introduction of CF effectively improves the mechanical properties of the MXene/CMP films, and the MXene nanosheets deposited on the surface of the film can reduce stress concentration, which in turn delays the expansion of cracks. Furthermore, the MXene/CMP sensor exhibits high sensitivity (89.76 kPa-1) and fast response/recovery time (61/60 ms). The deformation of microstructure and the construction of conductive networks enable the sensor to realize the monitoring of human motion signals (such as finger bending, knee bending, wrist bending, etc.). Meanwhile, the sensor maintains sensing stability even after 2000 pressure cycles, which can be attributed to the improvement in mechanical properties. In summary, the developed high-performance flexible pressure sensor with micropyramid structure has great application prospects in wearable devices and healthcare.

Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning

This paper proposes a machine learning- based system designed to predict mental health outcomes using wearable device data. The system is conceptualized to process physiological and behavioral data such as heart rate, sleep patterns, and activity levels collected from wearable technology. Key stages of the system include data preprocessing, feature extraction, and model training using multiple machine-learning algorithms, including Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. These models are combined using a voting-based ensemble classifier to improve prediction accuracy. While the system has not yet been implemented, expected results suggest that this approach will enhance prediction reliability and offer real-time insights into mental health conditions. The proposed system is envisioned to facilitate early detection of mental health disorders, thereby aiding in timely interventions and personalized care.

Innovative approaches to injury prevention and performance enhancement in running: insights from PhD research on hamstring strengthening, running biomechanics and energetics, and wearable technology (PhD Academy Award)

Innovative approaches to injury prevention and performance enhancement in running: insights from PhD research on hamstring strengthening, running biomechanics and energetics, and wearable technology (PhD Academy Award)

Using technology to support diabetes care in hospital: Guidelines from the Joint British Diabetes Societiesfor Inpatient Care (JBDS-IP) group and Diabetes Technology Network (DTN) UK.

This article summarises the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) Group guidelines on the use of technology to support diabetes care in hospital. The guideline incorporates two main areas: (i) use of wearable technology devices to improve diabetes management in hospital (including continuous glucose monitoring and insulin pump therapy) and (ii) information technology. Although it is reasonable to extrapolate from the evidence available, that devices developed to enhance diabetes care outside hospital will show similar benefits, there are challenges posed within the inpatient setting in hospital. This guidance provides a pragmatic approach to supporting self-management in individuals using wearable technology admitted to hospital. Furthermore, it also aims to provide a best practice guide for using information technology to monitor diabetes care and communicate between health professionals.

A systematic review on artificial intelligence approaches for smart health devices

In the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

Ethanolamine-modified gel electrolyte of zinc ion battery

The rapid growth of wearable electronic devices has spurred the development of flexible energy storage systems. However, the limitations of conventional rigid commercial batteries have hindered the advancement of wearable technology. Consequently, the development of stable, safe, highly flexible, and cost-effective flexible batteries has become a key focus in current battery research. Herein, we introduce a novel multifunctional flexible Prussian Blue–zinc battery utilizing a PVA-ZnCl₂ polymer gel electrolyte (EGPE) enhanced with ethanolamine (ETA), aimed at addressing the deficiencies of traditional electrolytes in terms of safety and mechanical performance. The Zn||Zn symmetric cell using EGPE electrolyte demonstrated an impressive long-term cycling stability of over 3000 hours at a current density of 0.15 mA cm−2. In contrast, the symmetrical cell using 0.8 M ZnCl2 aqueous electrolyte maintained its stability for only 400 hours. In addition, the prepared flexible zinc Prussian blue device maintained more than 65 % of its initial capacity of the device and its coulombic efficiency was close to 100 % after 400 cycles at an extreme current density of 10 A g−1. Furthermore, flexible device using the EGPE was able to maintain a stable power supply under various bending, torsion, folding and shearing conditions. Such innovative gel electrolyte demonstrated considerable prospects for utilisation in portably equipped devices and energy storage systems.

Designing High-Sensitivity Mechanochromic Luminescent Materials Through Friction-Induced Crystallization Strategy.

Despite recent significant breakthroughs in novel mechanochromic luminescent (ML) materials, developing a high-sensitivity ML material is still challengeable. Herein, a "friction-induced crystallization" strategy is proposed to realize highly sensitive transformations of luminescent signal, through an integration of polymeric chains and an aggregation-sensitive luminescent core, which act as mechanical sensors and fluochromic actuators, respectively. The coupling of these two components enables the material to crystallize in response to shear friction, thereby exhibiting blue-shift fluorescence due to a more restricted relaxation pathway. This study underscores a high-sensitivity ML material based on the precise regulation of molecular-scale motions, and also expands the scope and potential of ML materials toward user-friendly, interactive wearable devices.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

Mechanically Adaptable High‐Performance p(SBMA‐MMA) Copolymer Hydrogel with Iron (II/III) Perchlorate for Wearable Thermocell Applications

AbstractQuasi‐solid‐state thermocells (QTECs) show promise as a power source for wearable devices by converting body heat into electricity. While significant progress has been made with p‐type elements for QTECs, challenges remain with their n‐type counterparts. Here, a high‐performance n‐type QTEC element is presented, using a copolymer hydrogel that outperforms conventional p‐type elements. This copolymer hydrogel combines hydrophilic zwitterionic sulfobetaine methacrylate (SBMA) for ionic conduction and hydrophobic methyl methacrylate (MMA) for structural stability. By increasing the MMA content from 35 to 100 wt.%, versatile control is achieved over the hydrogel's elastic modulus (72 kPa to 127.7 MPa) and tensile strength (54 kPa to 6.7 MPa). A high mechanical toughness of 7.2 MJ m−3 is also achieved at 68 wt.% MMA. The mechanically robust and high toughness p(SBMA‐MMA) hydrogel is then immersed in Fe(ClO4)2/3 solutions of different concentrations to evaluate its performance as an n‐type QTEC element. The hydrogel with 0.8 M Fe(ClO4)2/3 exhibits a high ionic Seebeck coefficient of −1.7 mV K−1, a power density of 1.1 mW m−2 K−2, and an elastic modulus of 1.6 MPa, which is similar to that of human skin. Finally, the optimized n‐type p(SBMA‐MMA) hydrogels demonstrate the potential application for wearable devices.

Exploring Longitudinal Physiological Monitoring: Insights From Utilizing a Commercial Wearable Device Among Older Adults With Mild Cognitive Impairment

Commercial wearable technologies are becoming increasingly prevalent and offer the potential for monitoring physiological changes over time, particularly in older adults with Mild Cognitive Impairment (MCI). While literature suggests the utility of such devices, little evidence exists of their successful deployment among people with MCI. This feasibility study aimed to establish research guidelines, optimize participant training, and identify data collection challenges when collecting longitudinal physiological data using the Oura Ring. Our results indicated that adequate researcher support was crucial for study success and participant engagement. This involves including care partners of participants with MCI and providing appropriate technological training to participants. Care partner support reduced device troubleshooting instances and emotionally encouraged participants with MCI. Lastly, maintaining regular researcher-participant communication fostered rapport and ensured ongoing participant consent and data quality. This research lays the groundwork for future health monitoring studies by emphasizing the importance of tailored support, communication, and researcher involvement.

Special Issue on Robotics and Mechatronics Technology for Sports, Exercise, and Health Care (Part 2)

Robotics, mechatronics, and digital technologies are advancing significantly in sports, exercise, and healthcare, and offer innovative solutions that shape the future of these fields. In Part 2 of this special issue, we examine independent research and cutting-edge developments that address new challenges and provide unique contributions to these domains. Technological advancements in assistive devices and rehabilitation offer new solutions for improving the quality of life of individuals with physical challenges. Researchers of wearable assistive devices and prosthetics continue to devise methods to enhance mobility and support physical functions, thereby benefiting both users and healthcare providers. Sports and motion analysis remains a key focus area, where innovative technologies are employed to analyze and improve athletic performance. By capturing and evaluating detailed movement data, these technologies can help athletes refine their techniques and achieve greater precision in their sports, thus ultimately resulting in enhanced performance and injury prevention. As in Part 1, the studies presented in this issue demonstrate the practical applications of cutting-edge technologies and offer insights into their future potential. We hope that these contributions will inspire researchers and practitioners alike and provide a foundation for continued advancements in these exciting and impactful fields.

Development and Evaluation of a Wearable Upper Limb Assistive Device with a Remote Center of Motion Mechanism

Workers in factories, construction sites, and farms are often exposed to the risk of physical disability and injury. Particularly, the lower back and shoulders require assistance, as injuries can make it difficult to continue working. While social concern about back injuries is high and preventive measures are taken, lesser attention is paid to shoulders, resulting in inadequate assistance and prevention mechanisms. Therefore, wearable, low-cost, and lightweight upper limb assistive devices that can be used in multiple scenarios are desired by workers. In this study, we proposed a device that uses a remote center of motion (RCM) mechanism, enabling the device to support user’s upper arm from below, and the rotational center of device to correspond with shoulder joint. The theoretical assist torque is designed to be sufficient for arm gravity compensation of Japanese males of standard body size. The calculated theoretical assist torque for each shoulder joint is about 8 Nm and maximum assist force for each arm was about 36.3 N (3.7 kgf). Experimental evaluation using a prototype of this device on three healthy adult males demonstrated a decrease in muscle activity of approximately 38% in the anterior deltoid, 31% in the middle deltoid, and 11% in the posterior deltoid as an average of all results in a dynamic experiment in which the participants performed the indicated movements.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Using digital technologies and applications in midwifery practice in Belgium: A descriptive cross-sectional study

IntroductionOver the past two decades, there has been a dramatic increase in the use of digital technologies within healthcare and also in midwifery. However, scarce literature exist on the use of digital technologies and applications in midwifery practice particularly in the Belgian setting. AimTo map the use of digital technologies and applications by midwives in various settings, encompassing hospitals, primary care, and educational institutions in Belgium. MethodsA descriptive cross-sectional design was used to collect data via an online questionnaire targeting at midwives working in the Flemish region, Walloon region, or Brussels-Capital region. Midwives were either Dutch-speaking or French-speaking (n = 212). The semi-structured questionnaire addressed three domains: (1) socio-demographics, (2) employment information (3) the use of digital technology, including the level of comfort with technology. Qualtrics© program was used for managing data and SPSS v28.0© for data analysis using descriptive statistics, Chi² analysis, and independent sample t-test. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines. ResultsAmong respondents, 58% reported the use of digital technologies and applications, with electronic health records (89.6%), electronic prescriptions (65.6%), and remote patient monitoring (51.2%) commonly utilized. Sensor technology (7.2%), mixed care (3.8%), care robots (2.5%), and wearable devices (1.9%) were very rarely used. The target groups for these digital technologies and applications generally encompass all stages of the perinatal period. Midwives felt overall highly confident with these technologies. Younger participants and those with a Master's or PhD degree exhibited greater comfort scores in digital technology use. No significant relationships were observed between socio-demographic characteristics and the adoption of digital technology. ConclusionThe results highlight the potential for incorporating digital technologies and applications into various midwifery care settings. Addressing barriers and enhancing usability can optimize technology integration, contributing to personalized high-quality care, ultimately improving perinatal health outcomes.

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.

Encoding human activities using multimodal wearable sensory data

Human Activity Recognition (HAR) is the task to automatically analyze and recognize human body gestures or actions. HAR using time-series multi-modal sensory data is a challenging and important task in the field of machine learning and feature engineering due to its increasing demands in numerous real-world applications such as healthcare, sports and surveillance. Numerous daily wearable devices e.g., smartphones, smartwatches, and smart glasses can be used to collect and analyze the human activities on an unprecedented scale. This paper presents a generic framework to recognize the different human activities using continuous time-series multimodal sensory data of these smart gadgets. The proposed framework follows the channel of Bag-of-Features which consists of four steps: (i) Data acquisition and pre-processing, (ii) codebook computation, (iii) feature encoding, and (iv) classification. Each step in the framework plays a significant role to generate an appropriate feature representation of raw sensory data for efficient activity recognition. In the first step, we employed a simple overlapped-window sampling approach to segment the continuous time-series sensory data to make it suitable for activity recognition. Secondly, we build a codebook using k-means clustering algorithm to group the similar sub-sequences. The center of each group is known as codeword and we assume that it represents a specific movement in the activity sequence. The third step consists of feature encoding which transform the raw sensory data of activity sequence into its respective high-level representation for the classification. Specifically, we presented three reconstruction-based encoding techniques to encode sensory data, namely: Sparse Coding, Local Coordinate Coding, and Locality-constrained Linear Coding. The segmented activity sub-sequences are transformed to high-level representation using these techniques and earlier computed codebook. Finally, the encoded features are classified using a simple Random Forest classifier. The proposed HAR framework using three different encoding techniques is evaluated on three large benchmark datasets: UniMiB-SHAR dataset, MHEALTH dataset and WISDM dataset and their results are compared with most recent state-of-the-art techniques. It outperforms the existing techniques and achieves 98.39%, 99.5%, and 99.4% recognition scores on UniMiB-SHAR dataset, MHEALTH dataset, and WISDM dataset respectively. The excellent recognition results and computational analysis confirms the effectiveness and efficiency of the proposed framework.

The Role of Wearable Technology in Enhancing Patient Monitoring and Preventive Sexual and Reproductive Health Care

Wearable technology has emerged as a transformative tool in modern healthcare, offering enhanced opportunities for real-time patient monitoring and preventive care, particularly in the realm of sexual and reproductive health. This study aims to explore the role of wearable technology in improving patient outcomes through the continuous monitoring of health indicators and the provision of preventive care strategies. Using a qualitative approach, the research employs a systematic literature review and library research to analyze existing studies, reports, and scholarly articles. The findings indicate that wearable devices, such as fitness trackers and smartwatches, are increasingly integrated into sexual and reproductive health care for monitoring key indicators such as menstrual cycles, ovulation, and overall hormonal health. Additionally, wearables contribute to the early detection of potential health issues, including sexually transmitted infections (STIs) and irregular menstrual patterns, which can lead to more timely interventions. The integration of these technologies supports personalized health care, empowering patients to take an active role in their well-being. However, challenges such as data privacy concerns, accessibility, and the accuracy of these devices still need to be addressed to fully realize their potential in preventive sexual and reproductive health care. The study concludes that wearable technology offers significant potential to enhance patient monitoring and preventive care in sexual and reproductive health, but further research and development are required to overcome the existing limitations and ensure equitable access to these technologies.