Search Results (162)

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces a novel edge-computing wearable device for real-time beat-by-beat ECG arrhythmia classification. The proposed wearable integrates the light AI model into a 32-bit ARM^® Cortex^™-based custom printed circuit board (PCB). The work analyzes the performance of artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) models for real-time wearable implementation. The wearable is capable of real-time QRS detection and feature extraction from raw ECG data. The QRS detection algorithm offers high reliability with a 99.5% F1 score and R-peak position error (RPE) of 6.3 ms for R-peak-to-R-peak intervals. The proposed method implements a combination of top time series, spectral, and signal-specific features for model development. Lightweight, pretrained models are deployed on the custom wearable and evaluated in real time using mock data from the MIT-BIH dataset. We propose an LSTM model that provides efficient performance over accuracy, inference latency, and memory consumption. The proposed model offers 98.1% accuracy, with 98.2% sensitivity and 99.5% specificity while testing in real time on the wearable. Real-time inferencing takes 20 ms, and the device consumes as low as 5.9 mA of power. The proposed method achieves efficient performance in real-time testing, which indicates the wearable can be effectively used for real-time continuous arrhythmia detection. Full article

This study examines how advanced sports technologies, initially designed for elite athletes, are being applied in everyday contexts. Despite the proliferation of wearable and AI-powered tools, the sports management literature has largely overlooked how these innovations transition from professional use to consumer settings. Addressing this gap, the article evaluates key technologies based on cost, complexity, accessibility, and user-friendliness to determine their viability for broader adoption. The findings reveal a clear divide: while affordable, intuitive devices like WHOOP bands and Polar monitors are well-suited for general use, complex systems such as SportVU and VALD remain limited to elite environments. This study underscores simplicity, affordability, and contextual usability as critical enablers of adoption. By connecting theoretical innovation models with real-world patterns, this research offers practical guidance for developers, educators, and policymakers seeking to promote equitable access to sports technologies. Full article

Consumer-grade wrist-based wearable devices have grown in popularity among researchers to continuously collect metrics such as physical activity and heart rate. However, manufacturers rarely disclose the preprocessing sensor data algorithms, and user-generated data are typically shared leading to data governance issues. Open-source technology may address these limitations. This study evaluates the validity of the Bangle.js2 for step counting and heart rate during lab-based validation and agreement with other wearable devices (steps: Fitbit Charge 5; heart rate: Polar H10) in free-living conditions. A custom open-source application was developed to capture the sensor data from the Bangle.js2. Participants (n = 47; 25 males; 27 ± 11 years) were asked to complete a lab-based treadmill validation (3 min stages at 2, 3, 4, and 5 mph) and stair climbing procedure followed by a 24 h free-living period. The Bangle.js2 demonstrated systematic undercounting of steps at slower walking speeds with acceptable error achieved at 5 km/h. During free-living conditions, the Bangle.js2 demonstrated strong agreement with the Fitbit Charge 5 for per-minute step counting (CCC = 0.90) and total steps over 24 h (CCC = 0.96). Additionally, the Bangle.js2 demonstrated strong agreement with the Polar H10 for minute-averaged heart rate (CCC = 0.78). In conclusion, the Bangle.js2 is a valid open-source hardware and software solution for researchers interested in step counting and heart rate monitoring in free-living conditions. Full article

The recent fast advances in consumer electronics, especially in cell phones and displays, have led to the development of ultra-thin, hence flexible, glasses. Once available, such flexible glasses have proven to be of great interest and usefulness in other fields, too. Flexible photonics, for instance, has quickly taken advantage of this new material. At first sight, “flexible glass” appears to be an oxymoron. Glass is, by definition, fragile and highly breakable; its structure has puzzled scientists for decades, but it is evident that in most conditions it is a rigid material, so how can it bend? This possibility, however, has aroused the interest of artists and craftsmen since ancient times; thus, in Roman times the myth of flexible glass was born. Furthermore, the myth appeared again in the Middle Age, connected to a religious miracle. Today, however, flexible glass is no more a myth but a reality due to the fact that current technology permits us to produce micron-thick glass sheets, and any ultra-thin material can be bent. Flexibility is coming from the present capability to manufacture glass sheets at a tens of microns thickness coupled with the development of strengthening methods; it is also worth highlighting that, on the micrometric and nanometric scales, silicate glass presents plastic behavior. The most significant application area of flexible glass is consumer electronics, for the displays of smartphones and tablets, and for wearables, where flexibility and durability are crucial. Automotive and medical sectors are also gaining importance. A very relevant field, both for the market and the technological progress, is solar photovoltaics; mechanical flexibility and lightweight have allowed solar cells to evolve toward devices that possess the advantages of conformability, bendability, wearability, and moldability. The mature roll-to-roll manufacturing technology also allows for high-performance devices at a low cost. Here, a brief overview of the history of flexible glass and some examples of its application in solar photovoltaics are presented. Full article

Atrial fibrillation (AF) is a type of cardiac arrhythmia with a worldwide prevalence of more than 37 million among the adult population. This elusive disease is a major risk factor for ischemic stroke, along with increased rates of significant morbidity and eventual mortality. It is clinically diagnosed using medical-grade electrocardiogram (ECG) sensors in ambulatory settings. The recent emergence of consumer-grade wearables equipped with photoplethysmography (PPG) sensors has exhibited considerable promise for non-intrusive continuous monitoring in free-living conditions. However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. In this work, we present a deep learning framework that leverages convolutional layers with a bidirectional long short-term memory (CNN-BiLSTM) network and an attention mechanism for effectively classifying raw AF rhythms from normal sinus rhythms (NSR). We derive and feed heart rate variability (HRV) and pulse rate variability (PRV) features as auxiliary inputs to the framework for robustness. A larger teacher model is trained using the MIT-BIH Arrhythmia ECG dataset. Through transfer learning (TL), its learned representation is adapted to a compressed student model (32x smaller) variant by using knowledge distillation (KD) for classifying AF with the UMass and MIMIC-III datasets of PPG signals. This results in the student model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. Additionally, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to confer a notion of interpretability to the model decisions. We conclude that through a combination of techniques such as TL and KD, i.e., pre-trained initialization, we can utilize learned ECG concepts for scarcer PPG scenarios. This can reduce resource usage and enable deployment on edge devices. Full article

Human–computer interaction (HCI) enables communication between humans and computers, which is widely applied in various fields such as consumer electronics, education, medical rehabilitation, and industrial control. Human motion monitoring is one of the most important methods of achieving HCI. In the present work, a novel human motion monitoring sensor for contact touch and gesture recognition is fabricated based on polyester elastomer (PTE) synthesized from diols and diacids, with both piezoelectric and triboelectric properties. The PTE sensor can respond to contacted and contactless me-chemical signals by piezoelectric and triboelectric responding, respectively, which enables simultaneous touch control and gesture recognition. In addition, the PTE sensor presents high stretchability with elongation at break over 1000% and high durability over 4000 impact cycles, offering significant potential for consumer electronics and wearable devices. Full article

This paper presents a low-power photoplethysmography (PPG) readout system designed for wearable health monitoring. The system employs a differential current mirror (DCM) to convert single-ended PPG currents into differential voltages, inherently suppressing DC components. A wide common-mode input range (WCMIR) SAR ADC processes the differential signals, ensuring accurate analog-to-digital conversion. The DCM eliminates the need for DC cancelation loops, simplifying the design and reducing power consumption. Implemented in a 0.18 µm CMOS process, the system occupies only 0.30 mm², making it suitable for multi-channel applications. The system achieves over 60 dB DC dynamic range and consumes only 9.6 µW, demonstrating its efficiency for portable devices. The simulation results validate its ability to process PPG signals across various conditions, offering a scalable solution for advanced biomedical sensing platforms. Full article

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO₂) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO₂ is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient’s ability to increase cardiac output (CO). In addition, VO₂ measurements, particularly VO₂ max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO₂ assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO₂ continuously. Unlike conventional wearables that rely on static formulas for VO₂ max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer–Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R² of 0.94. By providing precise, individualized VO₂ estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments. Full article

In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity presents significant challenges in developing a highly accurate deep learning (DL) global model for ECG classification, as traditional centralized approaches struggle to address privacy concerns, scalability issues, and model inconsistencies arising from diverse device characteristics. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without sharing raw data, thus preserving privacy and security. However, standard FL assumes uniform device capabilities and model architectures, which is impractical given the varied nature of ECG data collection devices. Although heterogeneity has been explored in other domains, its impact on ECG classification and the classification of similar time series physiological signals remains underexplored. In this study, we adopted HeteroFL, a technique that enables model heterogeneity to reflect real-world resource constraints. By allowing local models to vary in complexity while aggregating their updates, HeteroFL accommodates the computational diversity of different devices. This study evaluated the applicability of HeteroFL for ECG classification using the MIT-BIH Arrhythmia dataset, identifying both its strengths and limitations. Our findings establish a foundation for future research on improving FL strategies for heterogeneous medical data, highlighting areas for further optimization and adaptation in real-world deployments. Full article

The home is an ideal setting for long-term sleep monitoring. This review explores a range of home-based sleep monitoring technologies, including smartphone apps, smartwatches, and smart mattresses, to assess their accuracy, usability, limitations, and how well they integrate with existing healthcare systems. This review evaluates 21 smartphone apps, 16 smartwatches, and nine smart mattresses through systematic data collection from academic literature, manufacturer specifications, and independent studies. Devices were assessed based on sleep-tracking capabilities, physiological data collection, movement detection, environmental sensing, AI-driven analytics, and healthcare integration potential. Wearables provide the best balance of accuracy, affordability, and usability, making them the most suitable for general users and athletes. Smartphone apps are cost-effective but offer lower accuracy, making them more appropriate for casual sleep tracking rather than clinical applications. Smart mattresses, while providing passive and comfortable sleep tracking, are costlier and have limited clinical validation. This review offers essential insights for selecting the most appropriate home sleep monitoring technology. Future developments should focus on multi-sensor fusion, AI transparency, energy efficiency, and improved clinical validation to enhance reliability and healthcare applicability. As these technologies evolve, home sleep monitoring has the potential to bridge the gap between consumer-grade tracking and clinical diagnostics, making personalized sleep health insights more accessible and actionable. Full article

Under the strategy of Healthy China, students’ physical health status not only affects their future life and studies but also influences social progress and development. By monitoring and measuring the daily PA levels of Chinese students over a week, this study aimed to fully understand the current PA status of students at different times, providing data support for improving students’ PA levels and physical health. (1) Wearable fitness trackers have advantages such as low cost, portable wearability, and intuitive test data. By exploring the differences between wearable devices and PA testing instruments, this study provides reference data to improve the accuracy of wearable devices and promote the use of fitness trackers instead of triaxial accelerometers, thereby advancing scientific research on PA and the development of mass fitness. A total of 261 students (147 males; 114 females) were randomly selected and wore both the Actigraph GT3X+ triaxial accelerometer and Huawei smart fitness trackers simultaneously to monitor their daily PA levels, energy metabolism, sedentary behavior, and step counts from the trackers over a week. The students’ PA status and living habits were also understood through literature reviews and questionnaire surveys. The validity of the smart fitness trackers was quantitatively analyzed using ActiLife software 6 Data Analysis Software and traditional analysis methods such as MedCal. Paired sample Wilcoxon signed-rank tests and mean absolute error ratio tests were used to assess the validity of the smart fitness trackers relative to the Actigraph GT3X+ triaxial accelerometer. A linear regression model was established to predict the step counts of the Actigraph GT3X+ triaxial accelerometer based on the step counts from the smart fitness trackers, aiming to improve the accuracy of human motion measurement by smart fitness trackers. There were significant differences in moderate-to-high-intensity PA time, energy expenditure, metabolic equivalents, and step counts between males and females (p < 0.01), with females having higher values than males in both moderate-to-high-intensity PA time and step counts. Sedentary behavior showed significant differences only on weekdays between males and females (p < 0.05), with females engaging in less sedentary behavior than males. (2) There was a significant difference in sedentary time between weekdays and weekends for students (p < 0.05), with sedentary time being higher on weekends than on weekdays. (3) Compared with weekends, female students had significantly different moderate-to-high-intensity PA time and sedentary time on weekdays (p < 0.01), while no significant differences were observed for male students. (4) Under free-living conditions, the average daily step count monitored by the smart fitness trackers was lower than that measured by the Actigraph GT3X+ triaxial accelerometer, with a significant difference (p < 0.01), but both showed a positive correlation (r = 0.727). (5) The linear regression equation established between the step counts monitored by the smart fitness trackers and those by the Actigraph GT3X+ triaxial accelerometer was y = 3677.3157 + 0.6069x. The equation’s R² = 0.625, with an F-test value of p < 0.001, indicating a high degree of fit between the step counts recorded by the Huawei fitness tracker and those recorded by the triaxial accelerometer. The t-test results for the regression coefficient and constant term were t = 26.4410 and p < 0.01, suggesting that both were meaningful. The tested students were able to meet the recommended total amount of moderate-intensity PA for 150 min per week or high-intensity PA for 75 min per week according to the “Chinese Adult PA Guidelines”, as well as the recommended daily step count of more than 6000 steps per day according to the “Chinese Dietary Guidelines”. (2) Female students had significantly more moderate-to-high-intensity PA time than male students, but lower energy expenditure and metabolic equivalents, which may have been related to their lifestyle and types of exercise. On weekends, female students significantly increased their moderate-to-high-intensity PA time compared with males but also showed increased sedentary time exceeding that of males; further investigation is needed to understand the reasons behind these findings. (3) The step counts monitored by the Huawei smart fitness trackers correlated with those measured by the Actigraph GT3X+ triaxial accelerometer, but the step counts from the fitness trackers were lower, indicating that the fitness trackers underestimated PA levels. (4) There was a linear relationship between the Huawei smart fitness trackers and the Actigraph GT3X+ triaxial accelerometer. By using the step counts monitored by the Huawei fitness trackers and the regression equation, it was possible to estimate the activity counts from the Actigraph GT3X+ triaxial accelerometer. Replacing the Actigraph GT3X+ triaxial accelerometer with Huawei smart fitness trackers for step count monitoring significantly reduces testing costs while providing consumers with intuitive data. Full article

Background: Consuming a variety of foods is believed to promote thorough chewing; however, it remains unclear whether individuals who consume various foods actually chew them thoroughly. Therefore, this study aimed to examine the association between dietary variety and masticatory behaviors, measured using wearable devices, among community-dwelling older adults. Methods: Participants were from the Itabashi Longitudinal Study of Aging, meeting the eligibility criteria, including the ability to exchange messages via smartphone or computer. Masticatory behaviors (number of chews, chewing duration, and speed) and meal photo data were objectively measured using an ear-worn bite sensor and its application for two or three meals per day for at least three days at home. The “modified Dietary Variety Score (m-DVS)” (range 0–10, with higher values indicating greater dietary variety) was calculated by registered dietitians. Generalized linear mixed models assessed the association between m-DVS as the exposure variable and masticatory behaviors as the outcome variable. Covariates included sociodemographic status, health behavior, health status, oral health, and oral function. Results: Five hundred and eighty-seven mealtime data entries from 63 participants were included in the analysis. The m-DVS was significantly positively associated with the number of chews (cycles, unstandardized regression coefficient = 116.5, 95% confidence interval [CI] = 85.2 to 147.8) and chewing duration (min, unstandardized regression coefficient = 1.7, 95% CI = 1.3 to 2.2). Conclusions: Consuming more varied food groups was associated with more chews and longer chewing duration among community-dwelling older adults, potentially promoting thorough chewing. Full article

With the rapid development of micro-nano technology and wearable devices, flexible photodetectors (PDs) have drawn widespread interest in areas such as healthcare, consumer electronics, and intelligence interfaces. Two-dimensional (2D) materials with layered structures have excellent optoelectronic properties and mechanical flexibility, which attract a great deal of attention in flexible applications. Although photodetectors based on mechanically exfoliated 2D materials have demonstrated superior performance compared to traditional Si-based PDs, large-scale manufacturing and flexible integration remain significant challenges for achieving industrial production. The emerging various printing technology provides a low-cost and highly effective method for integrated manufacturing. In this review, we comprehensively introduce the most recent progress on printed flexible 2D material PDs. We first reviewed the most recent research on flexible photodetectors, in which the discussion is focused on substrate materials, functional materials, and performance figures of merits. Furthermore, the solution processing for 2D materials coupled with printing functional film strategies to produce PDs are summarized. Subsequently, the various applications of flexible PDs, such as image sensors, healthcare, and wearable electronics, are also summarized. Finally, we point out the potential challenges of the printed flexible 2D material PDs and expect this work to inspire the development of flexible PDs and promote the mass manufacturing process. Full article

Obstructive Sleep Apnea (OSA) is a prevalent chronic sleep-related breathing disorder characterized by partial or complete airway obstruction. The expensive, time-consuming, and labor-intensive nature of the gold-standard approach, polysomnography (PSG), and the lack of regular monitoring of patients’ daily lives with existing solutions motivates the development of clinical support for enhanced prognosis. In this study, we utilize image representations of sleep stages and contextual patient-specific data, including medical history and stage durations, to investigate the use of wearable devices for OSA screening and comorbid conditions. For this purpose, we leverage the publicly available Wisconsin Sleep Cohort (WSC) dataset. Given that wearable devices are adept at detecting sleep stages (often using proprietary algorithms), and medical history data can be efficiently captured through simple binary (yes/no) responses, we seek to explore neural network models with this. Without needing access to the raw physiological signals and using epoch-wise sleep scores and demographic data, we attempt to validate the effectiveness of screening capabilities and assess the interplay between sleep stages, OSA, insomnia, and depression. Our findings reveal that sleep stage representations combined with demographic data enhance the precision of OSA screening, achieving F1 scores of up to 69.40. This approach holds potential for broader applications in population health management as a plausible alternative to traditional diagnostic approaches. However, we find that purely modality-agnostic sleep stages for a single night and routine lifestyle information by themselves may be insufficient for clinical utility, and further work accommodating individual variability and longitudinal data is needed for real-world applicability. Full article