Smart Bracelet Research Articles

The Relaxing Effect of Smart Bracelet on Heart Rate in Taekwondo Athletes Before Qualifying for Matches

Background: Anxiety disorders are among the most common mental health concerns in people’s lifestyles. There are many traditional psychological methods to manage anxiety. Heart rate is the most relevant biomarker of the autonomic nervous system among the various physiological markers. Hence, the increase in the heart rate is associated with a wide range of negative psychological events. Objectives: In this study, a wearable device called a "smart bracelet" was developed and tested to make mechanical oscillations similar to normal people’s heart rates, and then the effect of it was evaluated among taekwondo athletes wearing this device before the matches. Methods: In order to evaluate the results, the heart rate was measured five minutes before the competition, with an average of 81 bpm in the group using the bracelet and 84.42 bpm in the group without the bracelet. The average heart rate in the group with the bracelet was 78.5, and without a bracelet was 92.14 bpm. Results: The results of this study showed that wearable technology inducing mechanical waves is similar to normal people’s heartbeat through the radial nerve, which reduces stress significantly and can prevent an increase in stress.

Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras

Millimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing of the signals in the target environment is blind. We propose a method of using binocular vision to lock the distance dimension of the radar life signal and to determine the target distance by using the principle of the binocular camera parallax method, as this reduces the influence of the noise in the environment when determining the distance dimension of the target to be measured. First, the Yolo (you only look once: unified, real-time object detection) v5s neural network is used to call the binocular camera to detect the human body, where the resolution of the single lens is 1280 × 1200, and the DeepSORT (deep simple online real-time tracking) algorithm is used to extract the features of the target and track and register them. Additionally, the binocular vision parallax ranging method is used to detect the depth information of the target, search for the depth information in the range-dimensional FFT (frequency Fourier transform) spectrum of the radar echo signal, and take the spectral peak with the largest energy within the search range to determine it as the target. Then, the target is measured, the range gate of the target is determined, and the life signal is then separated through operations such as phase information extraction, unwrapping, and filtering. The test results showed that this method can be used to directionally separate and register corresponding life signals in a multiliving environment. By conducting an analysis using the Pearson correlation coefficient, we found that the correlation between the breathing frequency collected using this method and a breathing sensor reached 84.9%, and the correlation between the heartbeat frequency and smart bracelet results reached 93.6%. The target range gate was locked to separate and match the life signal.

A Wearable Gesture Recognition System With Ultrahigh Accuracy and Robustness Enabled by the Synergy of Multiple Fabric Sensing Devices

Recently, numerous wearable devices for gesture recognition have been reported, however, most of them are fabricated by assembling the separately-designed sensing layers with the substrate through linking agents, which will lead to the shifting or detaching of the sensing layers after long-duration use. Subsequently, over-reliance on multiple filtering algorithms to smooth the collected datasets becomes very popular, unavoidably resulting in the loss of data features and overfitting. In our point of view, it is much more crucial and effective for gesture recognition performance to reinforce the stability of the sensing layers. Therefore, in this work, we designed a type of Integrated MXene/Polyurethane (I-M/PU) sensor based on textile fabrics and further prepared a data glove, a smart wristband and a smart elbow pad. A wearable system for gesture recognition with ultra-high sensitivity to strain, as well as excellent stability and durability is successfully realized by the synergy of those three devices. The collected original data by the integrated sensors was steady with specific features, making the simplification of the classification model feasible. Finally, 19 static gestures (including 9 complex gestures) with the recognition accuracy of 100% in 6998 data samples have been impressively achieved by the simple Multilayer Perceptron (MLP) algorithm, which is the state-of-art best result as we know in the gesture recognition reports without any assistant computer vision algorithms, demonstrating our solution with a promising prospect in Virtual Reality (VR) and Human-Machine Interaction (HMI) fields.

Continuous User Authentication on Multiple Smart Devices

Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.

A Novel System to Monitor Tic Attacks for Tourette Syndrome Using Machine Learning and Wearable Technology: Preliminary Survey Study and Proposal for a New Sensing Device

Background Tourette syndrome is a neurological disorder that is characterized by repeated unintentional physical movement and vocal sounds, better known as tics. Cases of mild Tourette can have tics numerous times throughout the day, while severe cases may have tics every 5 to 10 seconds. At certain times, typically during high levels of stress, tics become chained in an incessant, continuous fashion—this is known as a tic attack. Tic attacks incapacitate the patient, rendering it difficult for them to move, perform daily actions, and even communicate with others. Caretakers—usually guardians, family members, or nurses—can help reduce the time tic attacks last with their presence and by providing emotional support to the patient. Objective We describe TSBand, a wearable wristband that uses machine learning algorithms and a variety of sensors to monitor for tic attacks and notify caretakers when an attack occurs. Methods We conducted a research survey with 70 Tourette patients to determine the usability and functionality of TSBand; internal review board approval was not required. Results This study has resulted in a smart wristband prototype that costs US $62.74; it uses movement, heart rate, sweat, and body temperature to detect tic attacks using a hybrid local outlier factoring and regression algorithm. An audio tic attack detection mechanism is also included, using recurrent neural networks, and a manually activated backup button and backup audio mechanism are fitted to alert caretakers on the personalized companion app. Conclusions TSBand enables the caretaker to provide support faster and prevent excessive self-harm or injury during the attack. It is an affordable and effective solution, solving a problem that many Tourette patients, often children, face. This study has not had the opportunity to test TSBand with any Tourette patients, and we aim to perform rigorous testing and analysis after grant funding is secured.

Design and Development of Online Medical System

Over the years, the health care system has significantly improved. However, with the technology available it is possible to integrate the medical services with online systems to make the patient’s life more convenient. Our application, Life Care, will help a patient to find a specialized doctor as per their needs, availability, distance, and consultancy charges. It is designed especially for an emergency and reduce the patient’s time in hunting for a specialized, doctor, smart technologies such as computers, smartphones, smart bracelets, smart medical devices can be used by medicinal organizations because of their enhanced usability, multiple uses, low costing, flexibility, mobility, reliability, and new capabilities. This encouraged health organizations to boost their demand for new healthcare technologies and enable users to multiple access medical services and improve the user experience.

Deception Attacks against Android-Based Smart Bracelets

The rapid development of the intelligent equipment industry has promoted the emergence of a series of emerging industries, effectively improving the technical level of society and people’s quality of life. Due to the inherent characteristics of smart devices, smart devices face serious privacy disclosure risks. Therefore, we have studied the security problem of the widely used smart device smart bracelet in this paper. We have adopted the attack method from easy to difficult to obtain the reading and writing instructions and timing characteristics of the intelligent devices to attack and steal private information. Specifically, we first attack through the strategy of log analysis; if this method is unable to obtain effective information, then we further acquire sensitive information on the basis of hook technology; and if the method on the basis of hook technology is still unable to obtain relevant information, then we will further use reverse engineering to conduct reverse analysis on the app to obtain sensitive information. Second, we develop a fake app on the basis of sensitive information and use it as a bridge to attack intelligent devices. In order to verify the effectiveness of the method, we successfully attacked and stole information from three popular business intelligence bracelets of different brands on the basis of the proposed method. The first step is to develop a fake app on the basis of the identified vulnerabilities. This app can bypass the protection measures of confusion and forced pairing and resetting to cheat the smart bracelet and can successfully enable or disable the jitters function remotely to modify the time and to obtain the sensitive data of the smart bracelet owner. In our attack process, we do not need the cooperation of the owner of the smart bracelet, nor do we need the target smart bracelet to match with our app.

Digital Cognitive Behavioral Therapy for Insomnia Using a Smartphone Application in China

Digital cognitive behavioral therapy for insomnia (DCBT-I) requires adaptation to different sociocultural contexts. Moreover, studies comparing DCBT-I and sleep education in the same operating interface are lacking. To investigate the efficacy of a smartphone-based Chinese culture-adapted DCBT-I application (app) for insomnia compared with sleep education using the same app. This was a single-blinded, randomized clinical trial conducted from March 2021 to January 2022. Screening and randomization were conducted at Peking University First Hospital. Follow-up visits were performed online or in the same hospital. After assessing for eligibility, eligible participants were enrolled and allocated (1:1) to DCBT-I or sleep education groups. Data were analyzed from January to February 2022. A Chinese smartphone-based app with the same interface was used in both DCBT-I and sleep education groups over 6 weeks, with 1-, 3-, and 6-month follow-ups. The primary outcome was Insomnia Severity Index (ISI) scores with the intention-to-treat principle. Secondary and exploratory outcomes included sleep diary measures; self-reported scales assessing dysfunctional beliefs about sleep, mental health, and quality of life; and smart bracelet measures. Of 82 participants (mean [SD] age, 49.67 [14.49] years; 61 [74.4%] females), with 41 randomized to sleep education and 41 randomized to DCBT-I; 77 participants completed the 6-week intervention (39 participants in the sleep education group and 38 participants in the DCBT-I group; full analysis data set) and 73 completed the 6-month follow-up (per protocol data set). Mean (SD) ISI scores in the DCBT-I group were significantly lower than those in the sleep education group after the 6-week intervention (12.7 [4.8] points vs 14.9 [5.0] points; Cohen d = 0.458; P = .048) and at the 3-month follow-up (12.1 [5.4] points vs 14.8 [5.5] points; Cohen d = 0.489; P = .04). There were significant improvements from before to after the intervention for both the sleep education and DCBT-I groups, with large effect sizes(sleep education: d = 1.13; DCBT-I: d = 1.71). Some of the sleep diary measures and self-reported scales showed more improvements in the DCBT-I group than sleep education group, such as total sleep time (mean [SD]: 3 months, 403.9 [57.6] minutes vs 363.2 [72.3] minutes; 6 months, 420.3 [58.0] minutes vs 389.7 [59.4] minutes) and sleep efficiency (mean [SD]: 3 months, 87.4% [8.3%] vs 76.7% [12.1%]; 6 months, 87.5% [8.2%] vs 78.1% [10.9%]). In this randomized clinical trial, the smartphone-based Chinese culture-adapted DCBT-I improved insomnia severity compared with sleep education. Future multicenter clinical trials with large sample sizes are needed to validate its effectiveness in the Chinese population. ClinicalTrials.gov Identifier: NCT04779372.

AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring.

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.

Design a Visually Impaired Walker Using an Internet of Thing -Based Smart Bracelet

The eyes are one of the most important senses in the human body. They are used to understanding the surrounding situation so people can understand what is around them. Visual impairment is divided into two types, total blindness and low vision, depending on the degree of disability. A standard walker is a stick that helps you assess the condition of the road and the obstacles in front of you. The developing electric mobility aids are not only walking sticks. A glove-shaped walker was developed to detect objects before the tent and provide information from beeps and vibrations. Produce a walker using a visually impaired smart bracelet using sensor technology to help the blind's vigilance and mobility capable of detecting objects at a predetermined distance. The tool successfully outputs information in human voices recorded in DFPlayer Mini according to the conditions of ultrasonic sensor readings and monitoring of smart bracelets through the blynk application. Operation There are buttons used to turn the system on and off. All sensor inputs and outputs will be processed using the ESP-8266 NodeMCU. From the test results of the entire system, it can be concluded that the smart bracelet runs optimally according to the block diagram the author has compiled. Write down the conclusions of your paper and further research suggestions in the form of narratives and not in bullet or numeral form.

Promoting Healthy Behaviors in Older Adults to Optimize Health-Promoting Lifestyle: An Intervention Study.

Introduction: Exercise intervention is the easiest and most effective way to promote human health. This study combined technology and exercise to improve the health behavior of the older adults through a physical activity intervention and to enhance a health-promoting lifestyle. Materials and methods: A quasi-experimental research method was used to openly recruit 120 healthy male and female older adults over 65 years old (average age of males: 71.6 ± 1.25 years; average age of females: 72.3 ± 1.28 years), all of whom wore smart bracelets. The participants were monitored by special personnel during the same period of walking every Monday to Friday. All participants recorded their daily steps, distance walked, and calorie consumption data for a period of 8 weeks. Results: After 8 weeks of walking, all participants showed a positive medium−high correlation of various factors between healthy behaviors and the health-promoting lifestyle scales. In the post-tests of each factor of two scales, males had the highest correlation between regular physical activity and physical activity, and females had the highest correlation between regular physical activity and social support. The variabilities in the explanatory power of the health behaviors of males and females on the health-promoting lifestyle were R2 = 70.9% (p < 0.01) and R2 = 74.1% (p < 0.01), indicating that the variables of healthy behaviors have a positive effect on health-promoting lifestyles in male and female older adults. Conclusions: Walking interventions positively affect the health behaviors of older adults and encourage health-promoting lifestyles. The value of this study is in its contribution to health promotion and public health recommendations for older adults.

OTFT Biosensor on Flexible Substrates for Human Health Monitoring: a Review

With the development of the Internet of Things era, smart wearable devices have gradually entered people’s daily life, and these devices have provided great convenience to human beings in terms of real-time detection of human health and information transmission. Smart wearable electronic devices have also been gradually developed from smart bracelets, smart watches, and smart glasses into fibers or textiles. Organic thin-film transistor (OTFT) as a key component of flexible electronic devices has an important role in signal amplification, signal filtering, signal transmission, signal reception, and storage. In this article, we introduce the recent progress of OTFT based on 2-D film and 1-D fiber from the substrate of OTFT and focus on the preparation method and performance of OTFT based on 1-D fiber from the conductive fiber. In addition, some practical applications of OTFT sensors in human health detection are introduced. Index Electronic textiles, organic thin-film transistor (OTFT), smart wearables.

Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions.

Human gesture interaction recognition algorithm based on augmented reality in smart wristband equipment

Human gesture interaction recognition algorithm based on augmented reality in smart wristband equipment

Human gesture interaction recognition algorithm based on augmented reality in smart wristband equipment

Human gesture interaction recognition algorithm based on augmented reality in smart wristband equipment

Retracted: Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis.

[This retracts the article DOI: 10.1155/2021/8347261.].

Retracted: Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network.

[This retracts the article DOI: 10.1155/2022/5597662.].

Application of Unconditioned Nanostructured Thermoplastic-Based Strain Gauge Sensor in Wearable Electronics

Wearable electronics is now revolutionizing the world of smart sensors offering tremendous solutions to a variety of applications that span from biomedical market to gaming and fashion sectors. In this scenario, physical sensors play a crucial role since they offer fast and reliable feedback on human motion, even for fine gestures, and can detect vital physiological parameters such as breathing and heart beating, while being able to be easily integrated into textile. Among wearable physical sensors, thermoplastic materials are utilized for their sensitivity and high stretchability. Moreover, these materials exhibit a good chemical resistance and implement low-cost manufacturing processes. In this work, we report a full characterization of a new thermoplastic nanocomposite material comparing its performances with and without preconditioning for strain up to 20%. Together with a measured gauge factor (GF) of about 10, sensors without preconditioning exhibit very good stability and they result to be a good candidate for wearable applications. We demonstrate this statement by analyzing the performance of a smart wristband prototype that integrates these strain gauges, obtaining very high performance of the sensors without prestrain in gesture recognition tasks with an accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}$ </tex-math></inline-formula> -score of about 94%.

Multitask Residual Shrinkage Convolutional Neural Network for Sleep Apnea Detection Based on Wearable Bracelet Photoplethysmography

Sleep apnea syndrome (SAS) is a common chronic respiratory disorder, which seriously harms human health. In order to realize the large-scale promotion of SAS detection, the SAS detection method based on wearable devices has attracted the attention of some researchers. However, in view of the poor signals collected by wearable devices and the less variation between signals of different classes (normal/SAS), the detection methods in past studies are often unsatisfactory. In this study, a wearable SAS detection method based on 1-D multitask multiattention residual shrinkage convolution neural network (1D-MMResSNet) model and cost-sensitive (CS) classifier was proposed. First, the photoplethysmography (PPG) sleep data of 92 subjects were collected by using wearable smart Bracelet devices. Second, a backbone network composed of the residual multiattention mechanism convolution block and residual shrinkage convolution block was proposed to realize feature selection and feature extraction for pulse rate variability signals. At the same time, the single supervised learning task of the network was improved, and a multitask learning method was proposed to enhance the ability of the network to mine subtle differences between data, thereby effectively learning the discriminative network features. Finally, the AdaCost cost-sensitive algorithm was introduced to alleviate the class imbalance problem in data samples. In segment detection, the accuracy, sensitivity, and specificity were 81.82%, 70.27%, and 85.81%, respectively; In individual detection, the accuracy, sensitivity, and specificity were 95.65%, 88.89%, and 97.30%, respectively. Experimental results show that the proposed model has excellent detection performance and is expected to be embedded in wearable devices.

Multifunctional Flexible Ionic Skin with Dual-Modal Output Based on Fibrous Structure.

Flexible wearable electronic devices with multiple sensing functions that simulate human skin in all aspects have become a popular research topic. However, the current expensive and time-consuming means of integration and the complex decoupling process are hampering the further development of multifunctional sensors. Here, an ultraflexible ionic fiber membrane (IFM) prepared by a simple electrospinning technique is reported, which exhibits pressure and humidity sensing properties. With the help of different electrode structures, the IFM-based multifunctional sensor achieved pressure and humidity detection with different sensing mechanisms. Pressure sensing with high sensitivity (49.7 kPa-1 at 0-30 kPa) and wide detection range (0-220 kPa) was indicated by the capacitive signal. Humidity sensing with high linearity (1.086% per percent relative humidity (RH)) in the range 15%-90% RH was indicated by the resistance signal. In particular, the multimodal output of capacitance/resistance corresponding to pressure/humidity in this study directly addresses the problem of accurately distinguishing the two stimuli. Furthermore, we have demonstrated that the impact between pressure and humidity is negligible when measured simultaneously and independently. Because of the excellent pressure/humidity sensing performance, we have fabricated a smart bracelet and mask for pulse, skin moisture, and breathe monitoring, which indicates the promising future of multifunctional flexible sensors based on IFM in the healthcare field.