Wearable Sensor Devices Research Articles

Community-dwelling older adults' acceptance of smartwatches for health and location tracking.

Despite rapid growth in the popularity of smartwatches, evidence lacks regarding older adults' acceptance of smartwatches. Since most wearable sensors are not designed specifically for older adults, there is a need to examine wearability and usability challenges of wearable sensing devices faced by older adults to facilitate the use of objective measurements of health and mobility. We aimed to examine older adults' perceptions of GPS-enabled smartwatches and to identify potential barriers and facilitators of smartwatch and sensor data use. As part of a larger feasibility study, we conducted a mixed-methods study that included a descriptive content analysis of interviews and a brief usability survey with 30 participants aged 60 years and older after they had used a smartwatch for 3 days. Most participants perceived wearable activity trackers including smartwatches and sensor-based data as useful for tracking health, finding activity patterns and promoting healthy behaviours. Privacy was of little concern, leading to willingness to share activity and location data with others. Participants identified barriers to usability as clumsy design, lack of aesthetic appeal, and difficulty reading the display and using the GPS tracking function. In contrast, identified facilitators of adoption included a big display, high-tech look, self-awareness and possible behaviour change. Smartwatches have the potential of personalised detection of health deterioration and disability prevention, based on analysis of older adults' activities in free-living environments. The usefulness of this technology for older adults can be significantly increased by addressing usability issues and providing instructions on challenging features. To support sustained self-monitoring behaviours through wearable sensor devices in older adults, it is critical to examine how they perceive those devices and identify factors affecting technology acceptance that can maximise adoption.

Affective State Recognition with Convolutional Autoencoders.

The aim of this study was to create a robust generalizable model to classify person's affective state based on physiological signals obtained using wearable sensor devices. Traditional machine learning methods require manual feature extraction from time sequences. Deep learning methods, such as Convolutional Neural Networks (CNN), can automatically extract features from time sequences. However, CNN models can be prone to overfitting, especially when the dataset is small. We apply a novel idea of using unsupervised convolutional autoencoders to automatically extract features from time-series data that are then fed to supervised classifier to classify people's affective state. We achieve almost 3% accuracy increase over traditional CNN model using all physio data from WESAD dataset, 2% increase using chest only physio data, and 8% increase using wrist only physio data while classifying neutral, stress, and amusement states. Code to reproduce the results can be found at https://github.com/srovins/wesad Clinical Relevance- A high-performing affective state recognition system can be utilized for various medical applications, ranging from patient monitoring to cognitive therapy.

Fabricating a hydroxynaphthalene benzophenone Schiff base into a wearable fluorescent sensor for point-of-care sensing of volatile organic compounds

We herein present a donor-acceptor (D-A) luminogen, (E)-(3- (((2-hydroxynaphthalen-1-yl)methylene)amino)phenyl)(phenyl)methanone (HNPMO), with both intramolecular charge transfer and aggregation-induced emission (ICT + AIE) properties. It was developed into a film sensor by drop-casting, exhibiting sharp increase in emission when exposing to various volatile organic compounds (VOCs), such as benzene series (e.g., toluene), common organic solvents (e.g., ethyl acetate) and alcohols (e.g., methanol), etc. Spectral analysis and quantum chemical calculations confirmed that the turn-on response can be ascribed to the transition from the disordered molecular arrangement to the optimized conformation driven by VOCs in the gas phase, with some major influence factors including volatility, molecular size, structural compatibility and steric hindrance. In addition, the fluorescent response is quantified by well-established image technology, using RGB and Lab color space. In the sensitivity test, Lab color shift (ΔELab) was used to estimate the limit of detection (LOD) of the HNPMO film sensor, which gives a linear relation with VOC concentration. The resulting LOD value is as low as 7.04 mg/m3 (1.8 ppm), suggesting high sensitivity intrinsic to such turn-on type sensor. We further fabricate a wearable sensor device based on HNPMO film, employing a reference substance which is inert to VOCs. Thus the presence of VOCs can be confirmed by the Lab color shift between HNPMO and the inert reference. By cutting and shaping, the film sensor applies to gloves, labor suits and even nails. It is capable of point-of-care detection of VOCs, providing an early warning of air contamination in various scenarios.

A Heartbeat Classifier for Continuous Prediction Using a Wearable Device

Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.

Vertically aligned conductive metal-organic framework nanowires array composite fiber as efficient solid-contact for wearable potentiometric sweat sensing

Fiber-type solid-contact ion-selective electrodes (SC-ISE) show unique superiorities for flexible and wearable sweat sensors with excellent wearing experiences. However, it is still challenging to construct high-quality fiber-type SC-ISE with robust sensing properties and facile fabrication methods. Here, we propose a new fiber-type flexible potentiometric sensor platform based on the conductive metal-organic framework (MOF) composite carbon nanotubes fiber (CNTF). The vertically aligned Nickel triphenylene-fused metal catecholate (NiCAT) nanowires arrays are in situ grown on CNTF through a bottom-up one-step hydrothermal synthetic approach. Nafion coating is designed to improve the surface hydrophobicity for avoiding the aqueous layer formation at the solid-contact=ion-selective-membrane interface. The well-defined solid-contact transducer of Nafion/NiCAT@CNTF exhibits the greatly enlarged double-layer capacitance (34.39 mF/cm2) attributed to its ordered porous architecture and low contact impedance. Consequently, the as-prepared Na+-ISE displays a sensitive near-Nernstian response of 58.69 ± 0.77 mV/dec, an excellent long-term potential stability with an ultralow drift of 8.1 ± 4.1 μV/h, and a good electrode-to-electrode reproducibility E0 of 308.0 ± 3.1 mV. An integrated wearable band-based sweat sensor device was developed for on-body sweat sodium monitoring during exercise. This platform provides an efficient and facile approach to construct reliable fiber-type potentiometric sensors for wearable applications.

A Batch Processing Technique for Wearable Health Crowd-Sensing in the Internet of Things

The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data processing and analytics. However, data sent to the cloud is vulnerable to on-path attacks. The bandwidth limitation issue is also another major problem during large data transfers. Moreover, the WHCS faces several anonymization issues. In light of this, this article presents a batch processing method to solve the identified issues in WHCS. The proposed batch processing method provides an aggregate authentication and verification approach to resolve bandwidth limitation issues in WHCS. The security of our scheme shows its resistance to forgery and replay attacks, as proved in the random oracle (ROM), while offering anonymity to users. Our performance analysis shows that the proposed scheme achieves a lower computational and communication cost with a reduction in the storage overhead compared to other existing schemes. Finally, the proposed method is more energy-efficient, demonstrating that it is suitable for the WHCS system.

Human Behavior Recognition in Outdoor Sports Based on the Local Error Model and Convolutional Neural Network.

With the rapid development of the Internet, various electronic products based on computer vision play an increasingly important role in people's daily lives. As one of the important topics of computer vision, human action recognition has become the main research hotspot in this field in recent years. The human motion recognition algorithm based on the convolutional neural network can realize the automatic extraction and learning of human motion features and achieve good classification performance. However, deep convolutional neural networks usually have a large number of layers, a large number of parameters, and a large memory footprint, while embedded wearable devices have limited memory space. Based on the traditional cross-entropy error-based training mode, the parameters of all hidden layers must be kept in memory and cannot be released until the end of forward and reverse error propagation. As a result, the memory used to store the parameters of the hidden layer cannot be released and reused, and the memory utilization efficiency is low, which leads to the backhaul locking problem, limiting the deployment and execution of deep convolutional neural networks on wearable sensor devices. Based on this, this topic designs a local error convolutional neural network model for human motion recognition tasks. Compared with the traditional global error, the local error constructed in this paper can train the convolutional neural network layer by layer, and the parameters of each layer can be trained independently according to the local error and does not depend on the gradient propagation of adjacent upper and lower layers. As a result, the memory used to store all hidden layer parameters can be released in advance without waiting for the end of forward and backward propagation, avoiding the problem of backhaul locking, and improving the memory utilization of convolutional neural networks deployed on embedded wearable devices.

Resilient bismuthene-graphene architecture for multifunctional energy storage and wearable ionic-type capacitive pressure sensor device

A facile resilient bismuthine-anchored graphene architecture is reported as multifunctional all-solid-state flexible supercapacitors and ionic-type capacitive sensor. Meanwhile, an electrons/ions dual transport channels design is achieved by inserting elaborately conductive bismuthene flakes into hierarchical porous aerogel framework. This strategy concurrently realizes the expansion of interlayer space for favoring electrolyte infiltration, and boost of interlayer conductivity to ensure interlayer electrons transport, endowing the device with attractive electrochemical energy storage and pressure sensing performance. As a result, the fabricated flexible symmetric supercapacitor device using bismuthene-graphene architecture as both negative and positive electrode delivers an excellent energy density of 45.55 Wh/kg at 400 W/kg along with cycling stability of 89.24% even after 3600 charge/discharge cycles. The bismuthene-graphene aerogel-based capacitive sensor with the hierarchical porous architecture demonstrates a high sensitivity of 0.326 kPa−1. Furthermore, the sensing mechanisms of ionic-type pressure sensor is explored. This work clearly demonstrates that the novel 3D hierarchical bismuthene-graphene architecture can be widely used in multifunctional devices of supercapacitors and tactile sensors.

An anti-freezing wearable strain sensor based on nanoarchitectonics with a highly stretchable, tough, anti-fatigue and fast self-healing composite hydrogel

The application of conductive hydrogel sensors in wearable devices and electronic skin has aroused great research interest. However, hydrogel sensor cannot simultaneously have good self-healing properties, anti-freezing properties and excellent anti-fatigue properties, which results in poor reusability and unstable sensing performance. A composite hydrogel was synthesized by one-pot method using polyvinyl alcohol, acrylamide, sodium alginate and glycerol as raw materials. The obtained hydrogel has excellent mechanical properties (0.51 MPa stress, 1500% elongation at break and tensile toughness of 3.6 MJ/m3) and fast self-healing performance with healing efficiency (HE) as high as 92% without any external stimulus. At the same time, glycerol has strong freeze resistance for hydrogels. The hydrogel can stably transmit electrical signals at subzero temperature (−20 °C). In addition, we also verified that the hydrogel could self-recover in a short time through cyclic stretching, indicating the fatigue resistance and rapid recovery of the material.

Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study.

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.

Review on hypertension diagnosis using expert system and wearable devices

<span>The popularity of smartphones and wearable devices is increasing in the global market. These devices track physical exercise records, heartbeat, medicines, and self-health diagnosis. The wearable devices can also collect personal health parameters include hypertension diagnosis. Hypertension is one of the risk factors for cardiovascular-related diseases among the Malaysian population. Many mobile applications are paired with wearable devices to monitor health conditions, but none of them able to diagnose hypertension. In this study, we reviewed research papers that focused on hypertension using expert systems and wearable devices. We performed a systematic literature review based on hypertension factors, expert systems, and wearable devices. We found 15 specific research papers after the filtering process. The key findings highlighted three main focuses, which are the factors of hypertension, the expert system techniques, and the types of sensors in wearable devices. Blood pressure is the most common factor of hypertension that can be collected by wearable devices. As for the expert system techniques, we determined the three most common techniques are machine learning, neural network, and fuzzy logic. Lastly, the wrist band is the most common sensor for wearable devices in hypertension-related research.</span>

Wearable Piezoelectric Yarns with Inner Electrodes for Energy Harvesting and Signal Sensing (Adv. Mater. Technol. 6/2022)

Piezoelectric Yarns In article number 2101138, Tae Hyun Sung and co-workers present an inner-electrode piezoelectric yarn (IEPY) that shows excellent durability, flexibility, and high signal accuracy through noise minimization which are required performance characteristics of the wearable sensor devices. The IEPY used during the process has excellent tensile strength and strong durability, maintaining its performance over 105 cycles under pushing force of 60 kg and generating excellent piezoelectric output energy.

Dual-Sensing, Stretchable, Fatigue-Resistant, Adhesive, and Conductive Hydrogels Used as Flexible Sensors for Human Motion Monitoring.

Hydrogel-based sensors serve as an ideal platform for developing personalized wearable electronics due to their high flexibility and conformability. However, the weak stretchability and inferior conductivity of hydrogels have severely restricted their large-scale application. Herein, a natural polymer-based conductive hydrogel integrated with favorable mechanical properties, good adhesive performance, and excellent fatigue resistance was fabricated via interpenetrating tannic acid (TA) into a chitosan (CS) cross-linked network in an acidic aqueous solution. The hydrogel was composed of a regular hierarchical porous structure, which was built by the hydrogen bonding between TA and CS. In addition, the hydrogels exhibited adjustable mechanical properties (maximum yield stress of 7000 Pa) and good stretchability (strain up to 320%). Benefiting from the abundant catechol groups of TA, the proposed hydrogels could repeatedly adhere to various material surfaces and could be easily peeled off without residue. Moreover, the hydrogel exhibited stable conductivity, high stretching sensitivity (gauge factor of 2.956), rapid response time (930 ms), and excellent durability (>300 cycles), which can be assembled as a strain sensor to attach to the human body for precise monitoring of human exercise behavior, distinguishing physiological signals, and recognizing speech. Furthermore, the prepared hydrogels also exhibited stable sensing performance to temperature. As a result, the hydrogels exhibited dual sensory performance for both temperature and strain deformation. It is anticipated that the incorporation of strain sensors and thermal sensors will provide theoretical guidance for developing multifunctional conductive hydrogels and pave a way for the versatile application of hydrogel-based flexible sensors in wearable devices and soft actuators.

Classification of Driver Cognitive Load: Exploring the Benefits of Fusing Eye-Tracking and Physiological Measures

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: a multi-stage approach

Purpose Post-stroke survivors report that feedback helps to increase training motivation. A wearable system (M-MARK), comprising movement and muscle sensors and providing feedback when performing everyday tasks was developed. The objective reported here was to create an evidence-based set of upper-limb tasks for use with the system. Materials and methods Data from two focus groups with rehabilitation professionals, ten interviews with stroke survivors and a review of assessment tests were synthesized. In a two-stage process, suggested tasks were screened to exclude non-tasks and complex activities. Remaining tasks were screened for suitability and entered into a categorization matrix. Results Of 83 suggestions, eight non-tasks, and 42 complex activities were rejected. Of the remaining 33 tasks, 15 were rejected: five required fine motor control; eight were too complex to standardize; one because the role of hemiplegic hand was not defined and one involved water. The review of clinical assessment tests found no additional tasks. Eleven were ultimately selected for testing with M-Mark. Conclusions Using a task categorization matrix, a set of training tasks was systematically identified. There was strong agreement between data from the professionals, survivors and literature. The matrix populated by tasks has potential for wider use in upper-limb stroke rehabilitation. IMPLICATIONS FOR REHABILITATION Rehabilitation technologies that provide feedback on quantity and quality of movements can support independent home-based upper limb rehabilitation. Rehabilitation technology systems require a library of upper limb tasks at different levels for people with stroke and therapists to choose from. A user-defined and evidence-based set of upper limb tasks for use within a wearable sensor device system have been developed.

Wearable sensor device for posture monitoring and analysis during daily activities: A preliminary study

The increase in technological advancements in recent years has led to the emergence of a new lifestyle. Although being assisted by machines for small-scale tasks in daily housework makes daily life easier, this has caused people to reduce their daily active movements and negatively affects human health. Especially during the COVID-19 pandemic, with the conversion of the working style to the home environment, working hours spent at the desk are more than ever. Due to the prolongation of the working time, the employees stay in the same position more inactive, thus their muscles weaken and they start to have muscle disease. Weaknesses in the muscles have occurred to the formation of postural problems in people. In our study, a smart vest system was developed to detect and control posture disorders. The proposed system is designed to recommend the most suitable exercises to avoid any physical discomforts. It is also aimed to detect hunched posture by collecting data on the person wearing the vest through sensors. Besides, it is encouraged to correct the posture disorder by warning the person audibly during the hunched posture. The experiments conducted with eight participants showed that the proposed system warns the users with necessary posture corrections, proving its potential use.

A Micro-Level Analysis of Physiological Responses to COVID-19: Continuous Monitoring of Pregnant Women in California.

Continuous monitoring of perinatal women in a descriptive case study allowed us the opportunity to examine the time during which the COVID-19 infection led to physiological changes in two low-income pregnant women. An important component of this study was the use of a wearable sensor device, the Oura ring, to monitor and record vital physiological parameters during sleep. Two women in their second and third trimesters, respectively, were selected based on a positive COVID-19 diagnosis. Both women were tested using the polymerase chain reaction method to confirm the presence of the virus during which time we were able to collect these physiological data. In both cases, we observed 3–6 days of peak physiological changes in resting heart rate (HR), heart rate variability (HRV), and respiratory rate (RR), as well as sleep surrounding the onset of COVID-19 symptoms. The pregnant woman in her third trimester showed a significant increase in resting HR (p = 0.006) and RR (p = 0.048), and a significant decrease in HRV (p = 0.027) and deep sleep duration (p = 0.029). She reported experiencing moderate COVID-19 symptoms and did not require hospitalization. At 38 weeks of gestation, she had a normal delivery and gave birth to a healthy infant. The participant in her second trimester showed similar physiological changes during the 3-day peak period. Importantly, these changes appeared to return to the pre-peak levels. Common symptoms reported by both cases included loss of smell and nasal congestion, with one losing her sense of taste. Results suggest the potential to use the changes in cardiorespiratory responses and sleep for real-time monitoring of health and well-being during pregnancy.

신축성 전도 섬유의 소프트 구동기 적용 및 성능 분석

In this study, soft actuators comprising conductive fibers, flexible polymers, and shape memory alloys, which can be used as textile products, are introduced. Conductive fibers play an important role because they can be used as sensors in wearable devices. The conductive fiber introduced in this study is a form that can be combined with a polymer, and it comprises a form wrapped around a flexible polymer. When an electric current is applied to the shape memory alloy embedded in the polymer, macroscopic deformation occurs due to phase transformation from the Martensite to the Austenite phase. Conductive fibers used in soft actuators are affected by resistive heat generated by the shape memory alloy and bending deformation of the actuator. Accordingly, changes in the conduction properties of conductive fibers were observed due to bending deformation and temperature changes. We also fabricated soft actuators with different types of polymers and observed the differences. The soft actuator presented in this study is a one-piece combination of a conductor and an actuator using a textile-type conductor, and it is likely to be used in smart clothing applications.

Direct printing of surface-embedded stretchable graphene patterns with strong adhesion on viscous substrates

The graphene patterns with piezoresistance behaviors, originating from the structural deformation and band-structure shift under strain, represent an attractive characteristic for developing the small strain (<10%) sensors for wearable devices. However, the insufficient adhesion between the patterns and substrates has significantly limited their utility and reliability. Here, the surface-embedded graphene patterns were directly deposited on polydimethylsiloxane (PDMS) surfaces without hydrophilic treatment via the embedded droplet printing (EDP). The viscous PDMS films instead of the solid ones were used as substrates, and the graphene patterns were partly embedded onto the viscous PDMS surfaces. To assess the adhesion performance, a series of tests were performed. In the bending and tensile tests, the patterns strongly adhered to the PDMS films. Further, the patterns had a favorable increase in resistance in the tensile strain range of 0–3.5%. The resistance of the surface-embedded graphene patterns exhibited a negligible change for over 3 min in the ultrasonic bath. Finally, the relative resistance R/R0 remained constant after the first two adhesion tests using a 3M tape. The surface-embedded graphene patterns exhibited a strong adhesion to the flexible/stretchable substrates, indicating the application prospect in the field of flexible devices.

The Deep Learning-based Human Activity Recognition Using Smart Wearable Sensors : A Tutorial

Human activity recognition (HAR) mechanisms that distinguish human behavior utilizing wearablesensors have advanced significantly over several years. Not only have state-of-the-art techniques ignoredhand-crafted features in favor of end-to-end deep learning approaches, but best practices fordesigning experiments, preparing datasets, and assessing activity recognition systems have changedin lockstep. This tutorial will provide an in-depth, hands-on introduction to the topic of sensor-basedHAR for those who are new to it. We will concentrate on deep learning-based HAR in this tutorialutilizing data from intelligent wearable sensor devices. This tutorial introduces the SDL-HARframework, which provides a general-purpose framework for data preprocessing, data generation,model development, and evaluation. We describe each aspect of the provided system in-depth, offerreferences to relevant research, and explain the community’s best practice methodologies for activityidentification. Two exemplary deep learning approaches, convolutional neural network (CNN) andlong short-term memory neural network (LSTM), are deployed in this lesson using state-of-the-artpublic HAR datasets. Additionally, this tutorial highlights the problems and future research directionsof sensor-based HAR.