Wearable Devices Research Articles

Observable, but Not Unobservable Health Numbers are Associated With Self-Reported Health: NHANES 2017-2020.

Present study explored the influence of self-reported health on diff between observable and unobservable health factors. Data obtained through the 2017-2020 pre-pandemic dataset of the National Health and Nutrition Examination Survey (NHANES). A series of analyses of covariance were conducted with self-reported health as the independent variable, and measures of blood pressure, waist-to-hip ratio, fasting glucose and total cholesterol as dependent variables. Family income served as a covariate. Results revealed significant differences in self-reported health and blood pressure, waist-to-hip ratio, and fasting glucose. Individuals who rated their health excellent or very good tended to have better health numbers than those who rated their health as fair or poor. No statistically significant difference was found with self-reported health and cholesterol. It is possible that health numbers that can be observed (or assessed) at home, without a health care professional, may be more aligned with how individuals perceive their own health. Health care professionals may want to further help patients recognize their own health status. The use of wearable technologies may provide added health information resulting in more accurate perception of health. A more accurate perception of a patient's own health may aid health care professionals in further improving patient care.

Lacticaseibacillus paracasei 207-27 alters the microbiota-gut-brain axis to improve wearable device-measured sleep duration in healthy adults: a randomized, double-blind, placebo-controlled trial.

Objective: Probiotics have been reported to exert beneficial effects on sleep through the gut-brain axis. Therefore, this randomized, double-blind, placebo-controlled trial assessed the effects of Lacticaseibacillus paracasei 207-27 supplementation on sleep quality and its safety and potential mechanisms. Method and study design: Healthy adults under mild stress aged 18-35 years consumed low or high doses of L. paracasei 207-27 or a placebo for 28 days. Fecal samples, blood samples, and questionnaires were collected at the baseline and the end of the intervention. Sleep quality was measured using wearable devices and Pittsburgh sleep quality index (PSQI) questionnaire. Serum inflammatory markers, corticotropin-releasing hormone, adrenocorticotropic hormone (ACTH), cortisol (COR), γ-aminobutyric acid, and 5-hydroxytryptamine levels were detected using enzyme-linked immunosorbent assay. The gut microbiota was analyzed using 16S rRNA sequencing and bioinformatics. Short-chain fatty acids levels were detected using gas chromatography-mass spectrometry. Results: Both the low-dose and high-dose groups exhibited significant improvements in wearable device- measured sleep duration compared to the placebo group. The global scores of PSQI in three groups significantly decreased after intervention without statistical difference between groups. At the phylum level, the low-dose group exhibited a higher relative abundance of Bacteroidota and a lower Firmicutes-to-Bacteroidetes (F/B) ratio. At the genus level, two treatment groups had higher relative abundance of Bacteroides and Megamonas, alongside lower levels of Escherichia-Shigella. Furthermore, the low-dose group exhibited significant increases in acetic acid, propionic acid, butyric acid, and valeric acid levels, while two treatment groups exhibited a significant decrease in COR levels. Correlation analysis revealed that the increased levels of acetic acid and butyric acid in the low-dose group may be associated with decreased ACTH. Conclusion: L. paracasei 207-27 administration in healthy adults resulted in improvements in gut microbiota community and sleep duration. The mechanisms might involve modulation of the gut microbiota structure to regulate the function of the gut-brain axis, including increases in SCFA levels and decreases in hypothalamic-pituitary-adrenal axis activity. The Chinese clinical trial registry number is ChiCTR2300069453 (https://www.chictr.org.cn/showproj.html?proj=191193, registered 16 May 2023 - retrospectively registered).

Metal Organic Frameworks Based Wearable and Point-of-Care Electrochemical Sensors for Healthcare Monitoring.

The modern healthcare system strives to provide patients with more comfortable and less invasive experiences, focusing on noninvasive and painless diagnostic and treatment methods. A key priority is the early diagnosis of life-threatening diseases, which can significantly improve patient outcomes by enabling treatment at earlier stages. While most patients must undergo diagnostic procedures before beginning treatment, many existing methods are invasive, time-consuming, and inconvenient. To address these challenges, electrochemical-based wearable and point-of-care (PoC) sensing devices have emerged, playing a crucial role in the noninvasive, continuous, periodic, and remote monitoring of key biomarkers. Due to their numerous advantages, several wearable and PoC devices have been developed. In this focused review, we explore the advancements in metal-organic frameworks (MOFs)-based wearable and PoC devices. MOFs are porous crystalline materials that are cost-effective, biocompatible, and can be synthesized sustainably on a large scale, making them promising candidates for sensor development. However, research on MOF-based wearable and PoC sensors remains limited, and no comprehensive review has yet to synthesize the existing knowledge in this area. This review aims to fill that gap by emphasizing the design of materials, fabrication methodologies, sensing mechanisms, device construction, and real-world applicability of these sensors. Additionally, we underscore the importance and potential of MOF-based wearable and PoC sensors for advancing healthcare technologies. In conclusion, this review sheds light on the current state of the art, the challenges faced, and the opportunities ahead in MOF-based wearable and PoC sensing technologies.

From wearables to performance: how acceptance of IoT devices influences physical education results in college students

With the advent of IoT technology in education, understanding its impact on physical education is crucial. This study investigates how the acceptance of wearable IoT devices influences the physical education results of college freshmen. It posits that user acceptance plays a mediating role in the effectiveness of these devices in enhancing physical performance metrics. The study enrolled 150 first-year students from Guangzhou University of Finance, divided equally into an experimental group and a control group. Participants in the experimental group were provided with ‘Xiaomi 8’ smart bracelets to be worn during physical education classes. The study spanned six weeks, focusing on assessing various physical performance metrics and the acceptance of the wearable technology. The data analysis involved comparing the physical performance of both groups and conducting regression analyses to evaluate the mediation effect of acceptance. Results indicated significant improvements in physical performance metrics in the experimental group, as evidenced by the Standardized Mean Differences (SMD). Notably, enhancements were observed in short-distance speed and aerobic endurance. The direct impact of wearable IoT devices on physical performance accounted for 66.4% variance, which increased to 84.1% upon incorporating acceptance as a mediator. These findings suggest that the effectiveness of wearable technology in physical education is significantly influenced by students’ acceptance. The study concludes that wearable IoT devices can effectively enhance physical education outcomes among college students, with user acceptance playing a crucial mediating role. This underscores the importance of considering user acceptance in the integration of technology in educational settings. The findings provide valuable insights for educators and technologists in designing and implementing technology-integrated curricula.

Application of ECG signal processing and classification based on CNN in wearable devices

Application of ECG signal processing and classification based on CNN in wearable devices

Accurate and 30-plus days reliable cuffless blood pressure measurements with 9-minutes personal photoplethysmograph data and mixed deduction learning

Cuffless blood pressure (BP) measurements have long been anticipated, and the PPG (Photoplethysmography)-only method is the most promising one since already embedded in many wearable devices. To further meet the clinical accuracy requirements, PPG-only BP predictions with personalized modeling for overcoming personal deviations have been widely studied, but all required tens to hundreds of minutes of personal PPG measurements for training. Moreover, their accurate test periods without calibration practice were not reported. In this work, we collected records of PPG data from our recruited subjects in real-life scenarios instead of relying on the openly available MIMIC dataset obtained from intensive care unit (ICU) patients. Since our objective is commercial application and a substantial reduction in training data, we tailored our model training to closely mimic real-world usage. To achieve this, we developed a training approach that only requires 9-minutes of personal PPG signal recordings and mixed with other PPG data from our recruited 364 subjects. The modeling is conducted with two-channel paired inputs to the convolutional neural network (CNN)-based model, which we called Mixed Deduction Learning (MDL). The test results of 88 samples from 15 subjects, under testing period up to 30-plus days without extra calibration, revealed that MDL meets most of the standards of AAMI, BHS, and IEEE 1708–2014 (for static test only) for BP measurement devices, which indicates MDL’s long-term stability and consistency. Furthermore, we found that the model with two-channel inputs presents a trend of improving performance as the pool of mixed training data increased, while the conventional one-channel input revealed degraded performance. The outperformance of MDL is attributed to many significant features remained in the first CNN layer even when mixing personal 9-minutes data with the other 364 subjects. Consequently, PPG-only with MDL introduces a new avenue for overcoming challenges in training due to personal physiological variations. Given our consideration of real-life usage, this technology can be seamlessly translated to commercial applications.

Feature selection based on game theory optimization to achieve desired performance metrics in seizure onset detection

Background and Objective: Epilepsy is one of the common diseases of the nervous system, which affects almost 1% of the world’s population. The unpredictable nature of epileptic seizures has caused people suffering from it to be deprived of their normal life, and making a device that can detect a seizure onset can help them. Methods:Game theory is a branch of mathematical science that, after proving its ability in economics, has entered into the field of signal processing and is trying to solve complex problems with a new approach. In this paper, we use game theory and coalitional game concepts to provide a novel way of feature selection in the analysis of Electroencephalography (EEG) signals. We have used a variety of statistical, morphological, and frequency features, and with the proposed feature selection algorithm we have attempted to select the smallest coalition which discriminates between two brain states while appropriately allocating resources to satisfy our desired sensitivity, delay, and false alarm. A modified change point detection algorithm has also been introduced to be used to detect epileptic seizures. Results:The results of the test on the CHB-MIT dataset show that using the proposed algorithm, we can achieve improved performance in terms of sensitivity, delay, and false alarm with a few number of features. Our framework also provides a means to focus on desired criteria for different applications. In scenario which the focus was on sensitivity, the proposed framework achieved 99% average sensitivity, 0.4/h average false alarm, and 2.4 s average delay with only using about 2 features for each patient. Conclusion:By applying the proposed framework on the CHB-MIT dataset we observe that we can achieve better performance compared to recent studies with fewer features in terms of sensitivity, delay, and false alarm metrics. Such an algorithm can be used for the purpose of real-time detection and in wearable devices.

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

In-silico user study case: wearable feedback haptic device for rehabilitation

In-silico user study case: wearable feedback haptic device for rehabilitation

Electromagnetic Interference Shielding Films: Structure Design and Prospects.

The popularity of portable and wearable flexible electronic devices, coupled with the rapid advancements in military field, requires electromagnetic interference (EMI) shielding materials with lightweight, thin, and flexible characteristics, which are incomparable for traditional EMI shielding materials. The film materials can fulfill the above requirements, making them among the most promising EMI shielding materials for next-generation electronic devices. Meticulously controlling structure of composite film materials while optimizing the electromagnetic parameters of the constructed components can effectively dissipate and transform electromagnetic wave energy. Herein, the review systematically outlines high-performance EMI shielding composite films through structural design strategies, including homogeneous structure, layered structure, and porous structure. The attenuation mechanism of EMI shielding materials and the evaluation (Schelkunoff theory and calculation theory) of EMI shielding performance are introduced in detail. Moreover, the effect of structure attributes and electromagnetic properties of composite films on the EMI shielding performance is analyzed, while summarizing design criteria and elucidating the relevant EMI shielding mechanism. Finally, the future challenges and potential application prospects of EMI shielding composite films are prospected. This review provides crucial guidance for the construction of advanced EMI shielding films tailored for highly customized and personalized electronic devices in the future.

Low-Contact Impedance Textile Electrode for Real-Time Detection of ECG Signals.

The quality of the electrocardiography (ECG) signals depends on the effectiveness of the electrode-skin connection. However, current electrocardiogram electrodes (ECGE) often face challenges such as high contact impedance and unstable conductive networks, which hinder accurate measurement during movement and long-term wearability. Herein, in this work, a bionic 3D pile textile as an ECGE with high electrical conductivity and flexibility is prepared by a facile, continuous, and high-efficiency electrostatic self-assembly process. Integrating pile textiles with conductive materials creates a full textile electrode for bioelectrical signal detection that can retain both the inherent characteristics of textiles and high conductivity. Moreover, the dense piles on the textile surface make full contact with the skin, mitigating motion artifacts caused by the sliding between the textile and the skin. The continuous conductive network formed by the interconnected piles allows the pile textile ECGE (PT-ECGE) to function effectively under both static and dynamic conditions. Leveraging the unique pile structure, the PT-ECGE achieves superior flexibility, improved conductivity, low contact impedance, and high adaptivity, washability, and durability. The textile electrode, as a promising candidate for wearable devices, offers enormous application possibilities for the unconscious and comfortable detection of physiological signals.

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification-Decoding Lateral Epicondylitis in Forearm Muscles.

Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase-amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject's label prediction. Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries-Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. This study's implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders.

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults

Preparation of aerogels with SBS@rGO structure by Pickering emulsion method for innovative applications in pressure sensors

Pressure sensors based on graphene conductive fillers show great promise for application in the field of smart wearable devices. The SBS@rGO structured aerogel sensors covered herein were prepared by the Pickering emulsion method based on a solution of styrene-butadiene-styrene (SBS) and reduced graphene oxide (rGO) dispersion. The mechanical properties of SBS@rGO aerogel were tested with 100 independent loading-unloading cycles in a graded compressive strain range of 10–40 %. The results indicate that the SBS@rGO aerogel have fast and slightly destructive resilience after unloading at low compressive strains (ε ≤ 30 %). Moreover, the presence of rGO endowed SBS@rGO with pressure sensing properties such as high electrical conductivity (2.294 S/m), high sensitivity (0.016 kPa−1 at 6.36–100 kPa), short signal response time (60 ms) and recovery time (30 ms). The SBS@rGO pressure sensors were integrated into shoe soles to simulate walking scenes, whose response signals had excellent reproducibility and could be differentiated according to three different walking states. The potential and feasibility of SBS@rGO aerogel as wearable flexible pressure sensors has been demonstrated.

Multifunctional MOF-5/Reduced Graphene Oxide Electrodes for Enhanced Nonenzymatic Glucose Detection and Supercapacitor Performance in Self-Powered Wearable Devices

Multifunctional MOF-5/Reduced Graphene Oxide Electrodes for Enhanced Nonenzymatic Glucose Detection and Supercapacitor Performance in Self-Powered Wearable Devices

Arrayed multi-layer piezoelectric sensor based on electrospun P(VDF-TrFE)/ZnO with enhanced piezoelectricity

Flexible pressure sensors can maintain good pressure sensing capabilities even under arbitrary degrees of deformation, which are widely used in fields such as robotic perception, physiological signal detection and wearable electronics. Currently, piezoelectric-based flexible pressure sensors face significant limitations in terms of large-area, high-sensitivity, and high-resolution pressure detection. Targeted research efforts primarily focus on enhancing the performance of piezoelectric sensors by optimizing the properties of the sensitive layer piezoelectric material and modulating device structural design. This study introduces a novel multilayer array design for flexible composite piezoelectric sensors. A high-voltage electrospinning selective deposition process is proposed to fabricate arrayed nanofiber mats. Patterned electrospun films can be automatically patterned by patterned AgNW electrodes, which allows manual alignment of patterns for subsequent stacking. Compared to sensor based on P(VDF-TrFE) prepared under the same conditions, the sensitivity of the multi-layer P(VDF-TrFE)/ZnO sensor increased from 2.82 mV/kPa to 8.30 mV/kPa, providing an effective approach for highly sensitivity wearable devices with short response time (∼5 ms).

Review of Liquid Metal Fiber Based Biosensors and Bioelectronics.

Liquid metal, as a novel material, has become ideal for the fabrication of flexible conductive fibers and has shown great potential in the field of biomedical sensing. This paper presents a comprehensive review of the unique properties of liquid metals such as gallium-based alloys, including their excellent electrical conductivity, mobility, and biocompatibility. These properties make liquid metals ideal for the fabrication of flexible and malleable biosensors. The article explores common preparation methods for liquid metal conductive fibers, such as internal liquid metal filling, surface printing with liquid metal, and liquid metal coating techniques, and their applications in health monitoring, neural interfaces, and wearable devices. By summarizing and analyzing the current research, this paper aims to reveal the current status and challenges of liquid metal conductive fibers in the field of biosensors and to look forward to their development in the future, which will provide valuable references and insights for researchers in the field of biomedical engineering.

Research Progress of Deep-Red to Near-Infrared Electroluminescent Materials Based on Organic Cyclometallated Platinum(II) Complexes.

In recent years, the near-infrared (NIR) light-emitting materials have attracted increasing attention due to the broad application prospects in the fields of military industry, aerospace, lighting, display and wearable devices. As the transition metal complexes, platinum(II) complexes have been shown to emit luminescence efficiently in NIR organic light-emitting diodes because of the unique d8 electron structure. This structure ensures that the platinum(II) complex molecules exhibit a high planarity, variety of excited states, and strong intermolecular interactions. This review summarizes the research progress of deep red to NIR organic light-emitting materials based on platinum(II) complexes in recent years and provides a certain reference for the further design and synthesis of NIR platinum(II) complex luminescent materials with superior performance.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

Hybrid rib fabric electromagnetic shielding measurement in X-band

This study involves measurement and evaluation of the electromagnetic shielding effectiveness (EMSE) in the X-band frequency range of rib-knitted fabrics formed from hybrid conductors. Hybrid conductive fabrics were made by knitting copper, silver, and stainless steel conductors together with cotton yarn. The basic theory of EMSE is introduced, and the waveguide measurement setup described. The through-reflect-line calibration setup was used to obtain accurate results. After calibration of the established waveguide measurement setup, hybrid conductive fabrics were tested. The measurements were analyzed and the fabrics compared. The aim is to produce a hybrid fabric that is suitable for general use, has a good EMSE, is easy to produce, and offers low-cost high-frequency solutions. An easy-to-produce and low-cost EMSE material is required, especially in new-generation wearable flexible electronic devices and military applications.