Smart Wristbands Research Articles

Statistical characteristics of large-scale objective tonic-clonic seizure records from medical smartwatches used in daily life.

This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic-clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts. We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates. Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90-1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08-26); (2) the linear relationship (slope = .79, R2 = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%). Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.

IoT-AI in Healthcare: A Comprehensive Survey of Current Applications and Innovations

The convergence of IoT and AI technology has the capacity to revolutionize healthcare by facilitating the gathering of real-time data and employing sophisticated analytics for tailored medical solutions. This survey provides an in-depth examination of IoT-AI applications in healthcare, specifically focusing on wearable devices such as smart bands and wristbands, as well as health monitoring systems. We present the core principles of IoT and AI, examining their synergistic integration in healthcare environments. The taxonomy of IoT-AI-based healthcare systems is comprehensive and classifies them according to their architectural components, data processing algorithms, and application domains. The survey showcases distinctive achievements, including novel methodologies for combining data and making predictions, frameworks for improving patient monitoring, and inventive methods for delivering healthcare remotely. We offer a comprehensive examination of key challenges such as data privacy, interoperability, and regulatory compliance, and analyze their specific effects on the implementation and efficacy of IoT-AI healthcare systems. The comparison analysis encompasses measures such as system performance, accuracy, and user satisfaction, providing valuable insights into the strengths and limitations of different techniques. In addition, we analyze developing patterns and clearly outline future areas of study, such as the enhancement of stronger security protocols, the use of blockchain technology to ensure data integrity, and the progress in AI algorithms to achieve more precise diagnoses. Emerging trends such as Digital Twins and SLUC are identified as promising avenues for future research. In conclusion, this study provides a detailed framework that enhances the understanding of IoT-AI healthcare systems and offers practical insights for improving healthcare practices and guiding technology adoption.

Exterior Design and Application of Intelligent Elderly Care Bracelet in the Era of Science and Technology

With the continuous development of the economy and society, various types of smart wristbands are emerging one after another, and the competition in the elderly care industry is becoming increasingly fierce. Among them, the convenient elderly care ability has become one of the main indicators to measure the competitiveness of elderly care. In the decades of development, the demand for elderly care in China has been increasing, and the ability of smart elderly care wristbands has been continuously improving. However, there are still many problems in their ability, which need to be further improved. This article takes the intelligent elderly care bracelet as the research object. Firstly, it explores the background and significance of the topic, summarizes and sorts out a series of theories and methods involved, and reviews the research status of intelligent elderly care at home and abroad. Then, it introduces the relevant theoretical basis for the ability of the bracelet we designed. Further analysis of the current situation of the elderly care bracelet industry.

Thermophysiological responses and thermal comfort of occupants in indoor spaces under different speaking and non-speaking conditions

As speaking is one of the fundamental behaviours in human activities, understanding the thermal comfort of occupants during speaking is crucial to providing a satisfactory environment. To further strengthen this understanding and identify how occupant thermal responses change during speaking activities, this study performs an experiment to examine the thermophysiological parameters and thermal comfort of occupants across three different speaking and non-speaking conditions: (1) silent (i.e., non-speaking); (2) speech (i.e., continuous speaking); and (3) conversation (i.e., intermittent speaking). During the experiment, thermophysiological parameters, including skin temperature, electrodermal activity, and heart rate, were collected using smart wristbands from 30 subjects at three different temperature settings to represent three distinct thermal environmental conditions: (1) Cold: 20 °C; (2) Neutral: 24 °C; and (3) Hot: 28 °C. Subjects were also required to report their thermal comfort perceptions in terms of thermal sensation votes, thermal preference votes, and thermal satisfaction votes. Repeated measures ANOVA test and Friedman test were used to analyse the thermophysiological parameters and thermal comfort perceptions. The null hypothesis, which suggests no significant differences in thermal responses and comfort across the three speaking conditions, will be rejected at a significance level of 0.05 (two-tail). The results revealed significant differences in all three thermophysiological parameters between speaking conditions at all temperature settings, while the thermal comfort perception did not consistently show statistically significant differences between speaking conditions. The findings contribute to our understanding of the relationship between communication and thermal responses, highlighting the importance of contextual factors in designing comfortable environments for speaking-related activities.

When it is part of me, I can do it: Using embodied empowerment to predict adherence to wearable self-care technology

Despite assumptions that wearable self-care technologies such as smart wristbands and smart watches help users to monitor and self-manage health in daily life, adherence rates are often quite low. In an effort to better understand what determines adherence to wearable self-care technologies, researchers have started to consider the extent to which a technology is perceived as being part of the user (i.e., technology embodiment) and the extent to which users feel they can influence reaching their health goals (i.e., empowerment). Although both concepts are assumed to determine adherence, few studies have empirically validated their influence. Furthermore, the relationships between technology embodiment, empowerment, and adherence to wearable self-care technology have also not been addressed. Drawing upon embodied theory and embodiment cognition theory, this research paper introduces and empirically validates ‘embodied empowerment’ as a predictor of adherence to wearable self-care technology. Using partial least squares structural equation modeling and multigroup analysis on a dataset of 317 wearable self-care technology users, we find empirical evidence of the validity of embodied empowerment as a determinant of adherence. We also discuss the implications for research and practice.

Microsphere-Structured Protein Hydrogel Dielectrics for Capacitive Wearable Sensors.

The desire for healthy living has created a crucial need for portable flexible health-monitoring devices based on biomaterials. Toward this end, we report a microsphere-structured hydrogel that uses bovine serum albumin (BSA) as a dielectric layer for capacitive pressure sensors. We developed a theoretical model that describes how stacking dielectric layers of spheres affects the performance of capacitive sensors. We also prepared a prototype sensor featuring the unique microsphere structure to create capacitive sensors with high sensitivity (360.91 strain sensitivity), excellent cyclical stability, and a long service life (over 5000 stretching-compression cycles). Furthermore, the design of the hydrogel sensor allows for easy integration into fabrics to create devices such as smart wristbands, which can collect a diverse range of health data. Thus, BSA-hydrogel-based sensors not only provide safe wearable devices but also advance the performance of high-sensitivity capacitive sensors.

Wireless Patient Monitoring System Based on Smart Wristbands and Central user Interface Software.

In this article, a patient monitoring system is proposed that is able to obtain heart rate and oxygen saturation (SpO2) levels of patients, identify abnormal conditions, and inform emergency status to the nurses. The proposed monitoring system consists of smart patient wristbands, smart nurse wristbands, central monitoring user interface (UI) software, and a wireless communication network. In the proposed monitoring system, a unique smart wristband is dedicated to each of the patients and nurses. To measure heart rate and SpO2 level, a pulse oximeter sensor is used in the patient wristbands. The output of this sensor is transferred to the wristband's microcontroller where heart rate and SpO2 are calculated through advanced signal processing algorithms. Then, the calculated values are transmitted to central UI software through a wireless network. In the UI software, received values are compared with their normal values and a predefined message is sent to the nurses' wristband if an abnormal condition is identified. Whenever this message is received by a nurse's wristband, an acoustic alarm with vibration is generated to inform an emergency status to the nurse. By doing so, health services are delivered to the patients more quickly and as a result, the probability of the patient recovery is increased effectively.

Investigation of the Validity and Reliability of Two Smart Bands Selected That Count Steps at Different Walking Speed

Whether activity monitor smart wristbands that have become widespread can accurately estimate the step count while walking is a matter of curiosity. The current study aims to analyze the validity and reliability of step count (SC) estimation at normal walking and fast walking pace under controlled conditions of two selected smart wristbands of the leading wearable device vendors, Xiaomi (Mi4) and Huawei (H4). Twenty healthy adult male and twenty healthy adult female were included in the study and analyzed separately. The mean age of male and female participants was 22.25 and 21.62 years, with BMI values of 24.22 kg/m2 and 21.42 kg/m2, respectively. The above-ground walking protocol consisted of four separate five-minute tests: Normal Walking Test, Normal Walking Retest, Fast Walking Test, and Fast Walking Retest. In the study, the analyses were performed by using activity monitor measurements and criterion measurements (the number of steps determined from video recordings), compatibility of test-retest measurement values, error indicators (MPE and MAPE), Intraclass Correlation Coefficients (ICC), and Bland-Altman limits of agreement. According to the current study results, it was revealed that the MAPE values recorded for Mi4 and H4 smart wristbands for both normal and fast walking pace in male and female participants were <5%, which was deemed excellent. According to all analyses, the H4 device was found to be valid and reliable, but according to ICC and Pearson Correlation analyses, the Mi4 device was not found to be valid and reliable at fast walking pace.

Assessment of Heart Rate Monitoring During Exercise With Smart Wristbands and a Heart Rhythm Patch: Validation and Comparison Study.

The integration of wearable devices into fitness routines, particularly in military settings, necessitates a rigorous assessment of their accuracy. This study evaluates the precision of heart rate measurements by locally manufactured wristbands, increasingly used in military academies, to inform future device selection for military training activities. This research aims to assess the reliability of heart rate monitoring in chest straps versus wearable wristbands. Data on heart rate and acceleration were collected using the Q-Band Q-69 smart wristband (Mobile Action Technology Inc) and compared against the Zephyr Bioharness standard measuring device. The Lin concordance correlation coefficient, Pearson product moment correlation coefficient, and intraclass correlation coefficient were used for reliability analysis. Participants from a Northern Taiwanese medical school were enrolled (January 1-June 31, 2021). The Q-Band Q-69 demonstrated that the mean absolute percentage error (MAPE) of women was observed to be 13.35 (SD 13.47). Comparatively, men exhibited a lower MAPE of 8.54 (SD 10.49). The walking state MAPE was 7.79 for women and 10.65 for men. The wristband's accuracy generally remained below 10% MAPE in other activities. Pearson product moment correlation coefficient analysis indicated gender-based performance differences, with overall coefficients of 0.625 for women and 0.808 for men, varying across walking, running, and cooldown phases. This study highlights significant gender and activity-dependent variations in the accuracy of the MobileAction Q-Band Q-69 smart wristband. Reduced accuracy was notably observed during running. Occasional extreme errors point to the necessity of caution in relying on such devices for exercise monitoring. The findings emphasize the limitations and potential inaccuracies of wearable technology, especially in high-intensity physical activities.

Extraction of evolutionary factors in smart manufacturing systems with heterogeneous product preferences and trust levels

Smart products, as an increasingly significant part of smart manufacturing systems, have brought users extreme experiences. To provide a significantly better user experience and succeed amidst intense market competition, smart products undergo continuous and iterative evolution guided by diverse user requirements. However, users' requirements are heterogeneous, ever-changing, and misleading owing to the heterogeneous familiarity and deceptive interactions, leading to an erroneous identification of product evolutionary factors and inferior subsequent evolution. In existing works, the heterogeneous preferences and trust levels of users are considered superficially, and the extracted product evolutionary factors fail to adequately fulfill users' requirements. To address this issue, a reliable extraction method for evolutionary factors of smart products with heterogeneous product preferences and trust levels is proposed. First, the fuzzy similarity measure is introduced and a group classification method based on this measure is designed to filter out users without reference value. Second, the trust score induced order weighted averaging (TS-IOWA) operator is employed to obtain the comprehensive trust level of an individual user. Third, the dual trust aggregating operator is improved by two corrective factors being incorporated with the Einstein product and the Einstein sum to overcome the deficiency: an over-reduction of trust level and an over-expansion of distrust level, and then employed to aggregate multiple trust/distrust values and sort the evolutionary factors. Finally, two case studies on the App rating system and the evolution of smart wristbands are provided to verify the effectiveness of this proposed method. Throughout the analysis, the ranking accuracy of users' trust levels has increased by 25%, and the matching ratio between the extraction results and user requirements has increased from 57.14% to 85.7% with an improvement of 50%. The results demonstrate that the proposed method is capable of obtaining more credible evolutionary factors of smart products.

The Adoption Intentions of Wearable Technology for Construction Safety

Wearable technology (WT) is vital for proactive safety management. However, the adoption and use of WTs are very low when it comes to construction safety. This study proposes a hybrid model, combining elements of the technology acceptance model and the theory of planned behaviour model, with the aim of determining the factors predicting the adoption intention of WTs for construction safety. A mixed-method approach was used to test the model, namely the structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results show that no single predictor can significantly drive the adoption intention of all six WTs, namely smart wearable sensors, smart safety hats, smart safety vests, smart insoles, smart safety glasses, and smart wristbands, except for the uncovered effective combinations based on each WT individually. This research contributes to new insights into the antecedents of the adoption intention of WTs for construction safety, which are also useful for other technologies.

POTENTIAL CONTRIBUTION OF HOME BLOOD PRESSURE TELEMONITORING (HBPTM), WITH CONCURRENT MEASUREMENT OF PHYSICAL ACTIVITY, TO AID THE CONTROL OF BOTH HYPERTENSION AND HEART FAILURE

Objective: Despite the comparability of HBPM and ABPM in majority of cases (ESH 2021), physicians do not trust their patients to measure their BP correctly. Our study has also showed a small consistent significant difference between week mean systolic BP in HBPTM and daytime ABPM (ca 3 mmHg), and no difference between HBPTM and ABPM/24h (Athens 2022). The aim of this presentation is to show the advantages of HBPTM for practice. Design and method: BP monitor Fora P30Plus (triplets of BP measurements in morning and evening) and mobile phone for data transfer are used. The required 5 minutes of physical inactivity before BPM and daytime physical activity are checked in by Xiaomi MiBand 2 smart wristbands. BP and HR values are accessible to patients and their physicians on a secure server. Results: Diani telemedicine system enables recording of all BPM with the marked insufficient rest before BPM, a calculation of week total, morning and evening mean BP with export of logbook in pdf, a day profile with BPM each hour to detect hypotension and daytime physical activity. It is easy to browse through week cycles of HBMTM, to record patient troubles and therapy changes (also by the patient). The system reports extreme BP and HR values (suitable for medication titration in heart failure) and occurrence of arrhythmia by e-mail to both physician and patient. It reports voice evaluation of actual BPM to the patient and gives him a signal to start a new BPM in case of insufficient rest before BPM. Conclusions: HBPTM in connection with a smart wristband improves the reliability of HBPM. The system reports extreme BP and HR values by e-mail and voice evaluation of actual BPM to the patient. HBPTM is suitable for long-term BP follow-up and is technically sound. It has a potential for control of both BP and rehabilitation in heart failure. Questions around regulations and financing remain to be resolved.

Large-Area Flexible Electrochromic Devices with High-Performance and Low-Power Consumption Enabled by Hydroxyhexyl Viologen-Substituted Polyhedral Oligomeric Silsesquioxane

In this work, we prepared octa-hydroxyhexyl viologen-substituted polyhedral oligomeric silsesquioxane (OHHV-POSS) as an organic–inorganic composite material applicable for large-area flexible electrochromic devices (FECDs). The organic–inorganic hybrid structure supported by the POSS cage and the enhanced interaction between hydroxyl end-groups and lithium salts can effectively increase the ionic conductivity of the ion gel films, thereby improving the electrochromic performance capabilities. Homogeneous ion gel was obtained with polyvinyl butyral and a lithium salt, and outstanding electrochromic properties were observed during electrochromic device operations. The FECDs of various sizes were fabricated for systematic evaluation, and the device of up to 22.5 × 19.5 cm2 was successfully constructed using the simple doctor blade coating method. The prepared large-area FECDs achieved a large transmittance difference of 70.0% at 604 nm, fast switching times of 21.1 and 18.0 s, high coloration efficiency of 260.61 cm2/C, long-term cycling stability of up to 300 cycles, and low power consumption of about 250 μW/cm2. Furthermore, the device was maintained stably in various application tests for heat insulation and storage, UV resistance, and mechanical stability. High performance and low power consumption of OHHV-POSS prove suitable for large-area FECDs, suggesting promising potential for smart windows, wristbands, and eyewear products.

Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces.

Recent advances in flexible wearable devices have boosted the remarkable development of devices for human-machine interfaces, which are of great value to emerging cybernetics, robotics, and Metaverse systems. However, the effectiveness of existing approaches is limited by the quality of sensor data and classification models with high computational costs. Here, a novel gesture recognition system with triboelectric smart wristbands and an adaptive accelerated learning (AAL) model is proposed. The sensor array is well deployed according to the wrist anatomy and retrieves hand motions from a distance, exhibiting highly sensitive and high-quality sensing capabilities beyond existing methods. Importantly, the anatomical design leads to the close correspondence between the actions of dominant muscle/tendon groups and gestures, and the resulting distinctive features in sensor signals are very valuable for differentiating gestures with data from 7 sensors. The AAL model realizes a 97.56% identification accuracy in training 21 classes with only one-third operands of the original neural network. The applications of the system are further exploited in real-time somatosensory teleoperations with a low latency of <1 s, revealing a new possibility for endowing cyber-human interactions with disruptive innovation and immersive experience.

COMPARISON OF AMBULATORY AND HOME BLOOD PRESSURE TELEMONITORING WITH PHYSICAL INACTIVITY MONITORING

Objective: Combined use of office BP (OBP) and out-of-office BP evaluation (ABPM or HBPM) is recommended according to recent guidelines. Aim of the study was to compare the results of BP measurement (BPM) by these methods while improving reliability of HBPTM by the physical activity monitoring. Design and method: BP monitor For a P30Plus was used both for office BP and 3 months home BP telemonitoring (HBPTM) study in triplets on each occasion. Frequency of ABPM (SEIVA TonoTrack) measurement was 15 min during day and 30 min during night. Patients were given preconfigured sets for HBPTM containing For a P30Plus, mobile phone and Xiaomi MiBand 2 smart wristband. 144 treated essential hypertensives aged 24–85 years, 60% males, were referred by their physicians as well controlled. Their therapy was not changed during this study. BP and HR values were accessible to patients and their physicians on a secured server. The demanded 5 minutes of physical inactivity before and during BPM were checked in informed patients by smart wristbands to increase the reliability of HBPTM. Undesired physical activity before and during BP measuring process was shown as the footmark symbol near to BP value in HBPTM logbook. Data were evaluated using a linear mixed model adjusted for gender, age, and BMI. Results: There was no significant difference in mean systolic BP (SBP) of both HBPT week cycle (132.6 ± 10.6 vs. 131.3 ± 9.9 mmHg) and daytime ABPM (135.8 ± 11.5 vs. 134.0 ± 10.6 mmHg) during the time period, respectively. Mean SBP of daytime ABPM was significantly higher (p &lt; 0.001) compared to mean SBP of HBPTM both at entry (135.8 ± 11.5 vs. 132.6 ± 10.6 mmHg) and exit (134.0 ± 10.6 vs. 131.3 ± 9.9 mmHg). Conclusions: Mean SBP of both HBPTM and daytime ABPM were consistent during the study. Daytime ABPM was consistently significantly higher by approximately 3 mmHg compared to HBPTM with the checked 5 min rest before BPM by smart wristbands. Our results suggest, that both methods are comparable.

Wearable Devices for Physical Monitoring of Heart: A Review.

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.

Automated classification of hand gestures using a wristband and machine learning for possible application in pill intake monitoring

BackgroundThanks to the increased interest towards health and lifestyle, a larger adoption in wearable devices for activity tracking is present among the general population. Wearable devices such as smart wristbands integrate inertial units, including accelerometers and gyroscopes, which can be utilised to perform automatic classification of hand gestures. This technology could also find an important application in automatic medication adherence monitoring. Accordingly, this study aims at comparing the performance of several Machine-Learning (ML) and Deep-Learning (DL) approaches for the automatic identification of hand gestures, with a specific focus on the drinking gesture, commonly associated to the action of oral intake of a pill-packed medication. MethodsA method to automatically recognize hand gestures in daily living is proposed in this work. The method relies on a commercially available wristband sensor (MetaMotionR, MbientLab Inc.) integrating tri-axial accelerometer and gyroscope. Both ML and DL algorithms were evaluated for both multi-gesture (drinking, eating, pouring water, opening a bottle, typing, answering a phone, combing hair, and cutting) and binary gesture (drinking versus other gestures) classification from wristband sensor signals. Twenty-two participants were involved in the experimental analysis, performing a 10 min acquisition in a laboratory setting. Leave-one-subject-out cross validation was performed for robust performance assessment. ResultsThe highest performance was achieved using a convolutional neural network with long- short term memory (CNN-LSTM), with a median f1-score of 90.5 [first quartile: 84.5; third quartile: 92.5]% and 92.5 [81.5;98.0]% for multi-gesture and binary classification, respectively. ConclusionsExperimental results showed that hand gesture classification with ML/DL from wrist accelerometers and gyroscopes signals can be performed with reasonable accuracy in laboratory settings, paving the way for a new generation of medical devices for monitoring medical adherence.

Cardiovascular Disease Risk Stratification in Wrist Wearable Devices and e-Health App Users: A Large-Scale Retrospective Study.

Introduction: Wearable devices, including smart wristbands and watches, are often used with e-health applications (apps). The users' characteristics of wrist wearable devices currently lack description, and the cardiovascular disease (CVD) high-risk rate of users remains unknown. Purpose: This study aimed to (1) describe the basic characteristics and habits of users of the "Amazfit Health" app and Huami wrist wearable devices and (2) analyze the proportion and define the population characteristics of users with a high risk of developing CVD. Subjects and Methods: This study included users >18 years of age, residing in mainland China, using the "Amazfit Health" app and Huami wearable devices. Devices data and users' self-reported information were collected in the app. The risk stratification was based on WHO/ISH cardiovascular risk prediction charts for the Western Pacific Region. Subjects with CVD history, total cholesterol ≥8 mmol/L, or ≥10% predicted CVD risk and those with <10% predicted CVD risk were considered to be at high and low risk of developing CVD, respectively. Results: Data were obtained from 80,098 (total users) and 10,866 users (subjects) for risk stratification. The age of the total users and subjects were 45.6 ± 15.4 and 50.7 ± 14.0 years, respectively. The number of male and female users was 50,024, and 30,074 in total users, and 7,284, and 3,582 in subjects, respectively. The body mass index of total users and subjects was 24.0 ± 4.6 kg/m2 and 24.6 ± 3.8 kg/m2, respectively. By classifying users' residences into first-tier cities, municipalities and provincial capitals, and other areas, the numbers of total users were 20,179, 28,213, and 31,137, and subjects were 2,587, 3,966, and 4,269, respectively. The number of subjects with high CVD risk was 1,161, accounting for 10.7% of all subjects. Conclusions: Users with high CVD risk only accounted for a small proportion of the population of wearable devices users.

Reconstructed silk fibroin mediated smart wristband for physiological signal detection

High sensitivity and biocompatibility have always been the performance pursued of Intelligent sensor of human health signals. One of the best ways in this regard is non-invasive, which requires wearable and ultra-sensitive biosensors. Mesoscopic reconstruction endows Silk fibroin (SF) with excellent mechanical properties, biocompatibility and thermostability by incorporating Polyurethane (PU) into the meso-structures of SF via intermolecular templated nucleation. The gelation process of SF is observed in situ to prove the inducing effect of PU in the self-assembly process of SF. The SF/PU co-crystal films are given conductivity by physical vapor deposition to obtain sensors and smart wristbands. The flexible pressure sensor shows a high sensitivity of 0.48 kPa−1 under the pressure from 0 Pa to 1500 Pa. The smart wristband can detect the body information reflected in the human wrist, such as the wrist meridian response caused by finger movement, and the pulse signals at the wrist.

Vibration warning design for reaction time reduction under the environment of intelligent connected vehicles

Scenario-based warnings for road safety can be provided in the environment of intelligent connected vehicles via Bluetooth earphones or smart wristbands; designing an optimal means of presentation to drivers is an important point of consideration. Vibration warnings have been widely studied owing to their unique benefits. This experimental study aims to identify suitable body parts for vibration warnings during driving. The independent variables were the vibration position (three levels of stimulus, i.e., wrist, shin, and upper jaw) and response effector (two levels, i.e., hand and foot). Experiment Tasks 1 and 2 measured participants’ simple reaction time and choice reaction time, respectively, when providing vibration warnings in non-driving situations. The results demonstrate that the vibration on the upper jaw has the shortest simple reaction time and choice reaction time. The effect of stimulus-response consistency on choice reaction time was insignificant. Task 3 was similar to Task 2, with the exception of simulated driving. Compared to the result in Task 2, the choice reaction time in Task 3 was approximately 200 ms longer. Vibration of the upper jaw was reported to have the highest perceived intensity and preference. Based on the study results, the design implications for wearable vibration warnings of collision avoidance systems are presented