Wearable Technologies for Healthy Ageing: Prospects, Challenges, and Ethical Considerations.

Digital technologies hold promise to modernize healthcare. Such opportunity should be leveraged also to address the needs of rapidly ageing populations. Against this backdrop, this paper examines the use of wearable devices for promoting healthy ageing. Previous work has assessed the prospects of digital technologies for health promotion and disease prevention in older adults. However, to our knowledge, ours is one of the first attempts to specifically address the use of wearables for healthy ageing, and to offer ethical insights for assessing the prospects of leveraging wearable devices in this context. We provide an analysis of the considerable opportunities associated with the use of wearables for healthy ageing, with a focus on the five domains of intrinsic capacity: locomotion, sensory functions, psychological aspects, cognition, and vitality. We then highlight current limitations and ethical challenges of such approach to healthy ageing, including issues related to access, inclusion, privacy, surveillance, autonomy, and regulation. We conclude by discussing the implications of our analysis in light of current debates on the ethics of digital health, and suggest measures to address the identified challenges.

[Development of Wireless Wearable Sleep Monitoring System Based on EEG Signal].

A wireless wearable sleep monitoring system based on EEG signals is developed. The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display. The system is small in size, low in power consumption, and light in weight. It can be worn on the patient's forehead and is comfortable. It can be applied to home sleep monitoring scenarios and has good application value. The key performance indicators of the system are compared with the industry-related medical device measurement standards, and the measurement results are better than the special standards.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition.

Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity's sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems.

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Portable Head-Mounted System for Mobile Forearm Tracking.

Computer vision (CV)-based systems using cameras and recognition algorithms offer touchless, cost-effective, precise, and versatile hand tracking. These systems allow unrestricted, fluid, and natural movements without the constraints of wearable devices, gaining popularity in human-system interaction, virtual reality, and medical procedures. However, traditional CV-based systems, relying on stationary cameras, are not compatible with mobile applications and demand substantial computing power. To address these limitations, we propose a portable hand-tracking system utilizing the Leap Motion Controller 2 (LMC) mounted on the head and controlled by a single-board computer (SBC) powered by a compact power bank. The proposed system enhances portability, enabling users to interact freely with their surroundings. We present the system's design and conduct experimental tests to evaluate its robustness under variable lighting conditions, power consumption, CPU usage, temperature, and frame rate. This portable hand-tracking solution, which has minimal weight and runs independently of external power, proves suitable for mobile applications in daily life.

Electromagnetic Energy Harvester Targeting Wearable and Biomedical Applications.

This work presents a miniaturized electromagnetic energy harvester (EMEH) based on two coils moving in a head-to-head permanent magnet tower. The two coils are separated by a set distance so that the applied force moves the EMEH from one equilibrium position to another. In this configuration, the harvester produces energy in two different working modes: when a force is applied to the moving part or when an external random acceleration is applied to the whole system. A custom test bench has been designed to characterize the behavior of this energy harvester under a variety of conditions encountered in wearable applications. Notably, at 10 Hz and 1.32 g RMS acceleration, our inertial EMEH demonstrates its capability to sustain a consistent output power of 1696 µW within a total volume of 22.39 cm3, showcasing its efficiency in environments with erratic stimuli typical of wearable and biomedical applications. The presented EMEH is compared with reported inertial EMEH structures to extract its design limitations as well as future improvements, situating the present work in a comprehensive state-of-the-art and defining a generic performance target for biomedical and wearable applications.

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System.

Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user's intention to move or muscle fatigue. These issues impede the user's ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review.

Background: Despite being the gold-standard method for objectively assessing sleep, polysomnography (PSG) faces several limitations as it is expensive, time-consuming, and labor-intensive; requires various equipment and technical expertise; and is impractical for long-term or in-home use. Consumer wrist-worn wearables are able to monitor sleep parameters and thus could be used as an alternative for PSG. Consequently, wearables gained immense popularity over the past few years, but their accuracy has been a major concern. Objective: A systematic review of the literature was conducted to appraise the performance of 3 recent-generation wearable devices (Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP) in determining sleep parameters and sleep stages. Methods: Per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, a comprehensive search was conducted using the PubMed, Web of Science, Google Scholar, Scopus, and Embase databases. Eligible publications were those that (1) involved the validity of sleep data of any marketed model of the candidate wearables and (2) used PSG or an ambulatory electroencephalogram monitor as a reference sleep monitoring device. Exclusion criteria were as follows: (1) incorporated a sleep diary or survey method as a reference, (2) review paper, (3) children as participants, and (4) duplicate publication of the same data and findings. Results: The search yielded 504 candidate articles. After eliminating duplicates and applying the eligibility criteria, 8 articles were included. WHOOP showed the least disagreement relative to PSG and Sleep Profiler for total sleep time (-1.4 min), light sleep (-9.6 min), and deep sleep (-9.3 min) but showed the largest disagreement for rapid eye movement (REM) sleep (21.0 min). Fitbit Charge 4 and Garmin Vivosmart 4 both showed moderate accuracy in assessing sleep stages and total sleep time compared to PSG. Fitbit Charge 4 showed the least disagreement for REM sleep (4.0 min) relative to PSG. Additionally, Fitbit Charge 4 showed higher sensitivities to deep sleep (75%) and REM sleep (86.5%) compared to Garmin Vivosmart 4 and WHOOP. Conclusions: The findings of this systematic literature review indicate that the devices with higher relative agreement and sensitivities to multistate sleep (ie, Fitbit Charge 4 and WHOOP) seem appropriate for deriving suitable estimates of sleep parameters. However, analyses regarding the multistate categorization of sleep indicate that all devices can benefit from further improvement in the assessment of specific sleep stages. Although providers are continuously developing new versions and variants of wearables, the scientific research on these wearables remains considerably limited. This scarcity in literature not only reduces our ability to draw definitive conclusions but also highlights the need for more targeted research in this domain. Additionally, future research endeavors should strive for standardized protocols including larger sample sizes to enhance the comparability and power of the results across studies.

Nickel-cobalt metal-organic frameworks based flexible hydrogel as a wearable contact lens for electrochemical sensing of urea in tear samples.

A flexible, wearable, non-invasive contact lens sensor utilizing nickel-cobalt metal-organic framework (Ni-Co-MOF) based hydrogel is introduced for urea monitoring in tear samples. The synthesized Ni-Co-MOF hydrogel exhibits a porous structure with interconnected voids, as visualized by Scanning Electron Microscopy (SEM). Detailed structural and vibrational properties of the material were characterized using X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, and Raman spectroscopy. The developed Ni-Co-MOF hydrogel sensor showcases a detection limit of 0.445 mM for urea within a linear range of 0.5-70 mM. Notably, it demonstrates exceptional selectivity, effectively distinguishing against interfering species like UA, AA, glucose, dopamine, Cl-, K+, Na+, Ca2+, and IgG. The enhanced electrocatalytic performance of the Ni-Co-MOF hydrogel electrode is attributed to the presence of Ni and Co, fostering Ni2+ oxidation on the surface and forming a Co2+ complex that acts as a catalyst for urea oxidation. The fabricated sensor exhibits successful detection and retrieval of urea in simulated tear samples, showcasing promising potential for bioanalytical applications. The binder-free, non-toxic nature of the Ni-Co-MOF hydrogel sensor presents exciting avenues for future utilization in non-enzymatic electrochemical sensing, including applications in wearable devices, point-of-care diagnostics, and personalized healthcare monitoring.

Advancing abiotic stress monitoring in plants with a wearable non-destructive real-time salicylic acid laser-induced-graphene sensor.

Drought and salinity stresses present significant challenges that exert a severe impact on crop productivity worldwide. Understanding the dynamics of salicylic acid (SA), a vital phytohormone involved in stress response, can provide valuable insights into the mechanisms of plant adaptation to cope with these challenging conditions. This paper describes and tests a sensor system that enables real-time and non-invasive monitoring of SA content in avocado plants exposed to drought and salinity. By using a reverse iontophoretic system in conjunction with a laser-induced graphene electrode, we demonstrated a sensor with high sensitivity (82.3 nA/[µmol L-1⋅cm-2]), low limit of detection (LOD, 8.2 µmol L-1), and fast sampling response (20 s). Significant differences were observed between the dynamics of SA accumulation in response to drought versus those of salt stress. SA response under drought stress conditions proved to be faster and more intense than under salt stress conditions. These different patterns shed light on the specific adaptive strategies that avocado plants employ to cope with different types of environmental stressors. A notable advantage of the proposed technology is the minimal interference with other plant metabolites, which allows for precise SA detection independent of any interfering factors. In addition, the system features a short extraction time that enables an efficient and rapid analysis of SA content.

Estimation of lower back muscle force in a lifting task using wearable IMUs.

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

Microfluidic Sensing Textile for Continuous Monitoring of Sweat Glucose at Rest.

Wearable sweat sensors have received considerable attention due to their great potential for noninvasive continuous monitoring of an individual's health status applications. However, the low secretion rate and fast evaporation of sweat pose challenges in collecting sweat from sedentary individuals for noninvasive analysis of body physiology. Here, we demonstrate wearable textiles for continuous monitoring of sweat at rest using the combination of a heating element and a microfluidic channel to increase localized skin sweat secretion rates and combat sweat evaporation, enabling accurate and stable monitoring of trace amounts of sweat. The Janus sensing yarns with a glucose sensing sensitivity of 36.57 mA cm-2 mM-1 are embroidered into the superhydrophobic heated textile to collect sweat directionally, resulting in improved sweat collection efficiency of up to 96 and 75% retention. The device also maintains a highly durable sensing performance, even in dynamic deformation, recycling, and washing. The microfluidic sensing textile can be further designed into a wireless sensing system that enables sedentary-compatible sweat analysis for the continuous, real-time monitoring of body glucose levels at rest.

Eye-Readable and Wearable Colorimetric Sensor Arrays for In Situ Monitoring of Volatile Organic Compounds.

Wearable sensors utilize changes in color as a response to physiological stimuli, making them easily recognizable by the naked eye. These colorimetric wearable sensors offer benefits such as easy readability, rapid responsiveness, cost-effectiveness, and straightforward manufacturing techniques. However, their applications in detecting volatile organic compounds (VOCs) in situ have been limited due to the low concentration of complex VOCs and complicated external interferences. Aiming to address these challenges, we introduced readable and wearable colorimetric sensing arrays with a microchannel structure and highly gas-sensitive materials for in situ detection of complex VOCs. The highly gas-sensitive materials were designed by loading gas-sensitive dyes into the porous metal-organic frameworks and further depositing the composites on the electrospun nanofiber membrane. The colorimetric sensor arrays were fabricated using various gas-sensitive composites, including eight dye/MOF composites that respond to various VOCs and two Pd2+/dye/MOF composites that respond to ethylene. This enables the specific recognition of multiple characteristic VOCs. A microfluidic channel made of polydimethylsiloxane (PDMS) was integrated with different colorimetric elements to create a wearable sensor array. It was attached to the surface of fruits to collect and monitor VOCs using the DenseNet classification method. As a proof of concept, we demonstrated the feasibility of the wearable sensing system in monitoring the ripening process of fruits by continuously measuring the VOC emissions from the skin of the fruit.

Dual-band MIMO antenna with low mutual coupling for 2.4/5.8 GHz communication and wearable technologies.

To satisfy the requirements of modern communication systems and wearables using 2.4/5.8 GHz band this paper presents a simple, compact, and dual-band solution. The antenna is extracted from a circular monopole by inserting various patches and stubs. The genetic algorithm is utilized to optimize the parameters and achieve the best possible results regarding bandwidth and gain. Afterward, a 2-port multiple-input-multiple-output (MIMO) configuration is created by positioning an identical second single element perpendicularly to the first one. The electrical size of the suggested MIMO configuration is 0.26 λL × 0.53 λL, where λL represents the free space wavelength at lower resonance of 2.45 GHz. The common ground technique is adopted to further reduce and achieve the accepted level of mutual coupling of the MIMO configuration. The presented MIMO antenna offers a low mutual coupling of < -27 dB with 0.2 envelope correlation coefficient (ECC). The antenna has a gain of around 6.2 dBi and 6.5 dBi at resonating frequencies of 2.45 GHz and 5.4 GHz. Furthermore, the specific absorption rate (SAR) analysis of the MIMO antenna offers a range inside of the standard values, showing its potential for On/Off body communications. The comparison with already published works shows that the proposed antenna achieves better results in either compact size or wide operational bandwidth along with low mutual coupling.

Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions.

Conventional snap fasteners used in clothing are often used as electrical connectors in e-textile and wearable applications for signal transmission due to their wide availability and ease of use. Nonetheless, limited research exists on the validation of these fasteners, regarding the impact of contact-induced high-amplitude artefacts, especially under motion conditions. In this work, three types of fasteners were used as electromechanical connectors, establishing the interface between a regular sock and an acquisition device. The tested fasteners have different shapes and sizes, as well as have different mechanisms of attachment between the plug and receptacle counterparts. Experimental evaluation was performed under static conditions, slow walking, and rope jumping at a high cadence. The tests were also performed with a test mass of 140 g. Magnetic fasteners presented excellent electromechanical robustness under highly dynamic human movement with and without the additional mass. On the other hand, it was demonstrated that the Spring snap buttons (with a spring-based engaging mechanism) presented a sub-optimal performance under high motion and load conditions, followed by the Prong snap fasteners (without spring), which revealed a high susceptibility to artefacts. Overall, this work provides further evidence on the importance and reliability of clothing fasteners as electrical connectors in wearable systems.

[Preliminary observation of wearable balance diagnosis and treatment system in evaluating dynamic and static balance function in patients with vestibular vertigo].

A newly developed wearable balance diagnosis and treatment system was studied to evaluate the indexes of the abnormal balance function in patients with vestibular vertigo. A cross-sectional study was carried out. A total of 30 patients diagnosed with non-acute vestibular vertigo in the outpatient department of Eye, Ear, Nose and Throat Hospital Affiliated to Fudan University from July 2022 to May 2023 were selected as the vertigo group, including 13 males and 17 females, and aged (45.7±13.9) years. Meanwhile, 20 healthy controls (8 males and 12 females) were included as the control group, with a mean age of (43.6±8.0) years. The static balance and limits of stability (LOS) function of all subjects were assessed with wearable balance diagnosis and treatment system developed under the leadership of Eye & ENT Hospital of Fudan University. In the static balance test, the ratio of eyes open with cushions to eyes open without cushions in the vertigo group was less than that of the control group[1.20% (0.92%, 1.53%) vs 1.49% (1.22%, 1.81%), P=0.008], indicating that patients with non-acute vestibular vertigo may compensate static balance ability earlier. In vertigo group, the directional control in 8 directions, the maximum excursion in anterior, posterior, right anterior and right posterior directions, the endpoint excursion in the posterior, right posterior, and left posterior directions were all smaller than those of the control group (all P<0.05). The reaction time in the left posterior direction of vertigo group was longer than that of the control group (all P<0.05). Those results indicated that the directional control, maximum excursion and endpoint excursion of LOS could be considered as important reference indexes for dynamic balance function.

MultiSenseBadminton: Wearable Sensor-Based Biomechanical Dataset for Evaluation of Badminton Performance.

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors.

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.