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The aim of this study was to investigate the feasibility of automatically assessing the 2-Minute Walk Distance (2MWD) for monitoring people with multiple sclerosis (pwMS). For 154 pwMS, MS-related clinical outcomes as well as the 2MWDs as evaluated by clinicians and derived from accelerometer data were collected from a total of 323 periodic clinical visits. Accelerometer data from a wearable device during 100 home-based 2MWD assessments were also acquired. The error in estimating the 2MWD was validated for walk tests performed at hospital, and then the correlation (r) between clinical outcomes and home-based 2MWD assessments was evaluated. Robust performance in estimating the 2MWD from the wearable device was obtained, yielding an error of less than 10% in about two-thirds of clinical visits. Correlation analysis showed that there is a strong association between the actual and the estimated 2MWD obtained either at hospital (r = 0.71) or at home (r = 0.58). Furthermore, the estimated 2MWD exhibits moderate-to-strong correlation with various MS-related clinical outcomes, including disability and fatigue severity scores. Automatic assessment of the 2MWD in pwMS is feasible with the usage of a consumer-friendly wearable device in clinical and non-clinical settings. Wearable devices can also enhance the assessment of MS-related clinical outcomes.

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88.79% (without Stage 1 (data size reducing convolutional layer)), 90.58% (with 1 × 3 Stage 1), and 91.77% (with 1 × 5 Stage 1).

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

The intelligent wearable travel aid device has been developed for blind person usage for traveling purposes. The intelligent wearable travel aid device will be used along with the long cane that is usually used to detect any obstructions around the blind person. However, the problem on power supply to supply the electrical energy for the intelligent wearable travel aid device to work properly always been occurred. In order to fit the energy harvesting device on the intelligent wearable travel aid device, the comparison of the solar panel and photodiode is done. The performance evaluation to compare theenergy harvesting method on the developed intelligent wearable travel aid device for blind person has been conductedbased on the experiment result. The photodiode is proposed in this study due to small size and easy to arrange on top of developed wearable travel aid device compared to the solar panel which big size but commonly used as energy harvesting device. Consequently, the experimental result of the intelligent wearable travel aid device in terms of voltage, current and light intensity for the improved version with different type of configuration is proven respectively.

Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high stress contributes to major health issues such as heart disease, high blood pressure, diabetes, depression, and anxiety. Several researchers have focused on detecting stress through combining many features with machine/deep learning models. Despite these efforts, our community has not agreed on the number of features to identify stress conditions using wearable devices. In addition, most of the reported studies have been focused on person-specific training and testing. Thanks to our community’s broad acceptance of wearable wristband devices, this work investigates a global stress detection model combining eight HRV features with a random forest (RF) algorithm. Whereas the model’s performance is evaluated for each individual, the training of the RF model contains instances of all subjects (i.e., global training). We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases) and their combination. The eight HRV features with the highest classifying power are selected using the minimum redundancy maximum relevance (mRMR) method, reducing the training time of the global stress platform. The proposed global stress monitoring model identifies person-specific stress events with an accuracy higher than 99% after a global training framework. Future work should be focused on testing this global stress monitoring framework in real-world applications.

What are the effects of frequency variation of vibrotactile stimuli on localization acuity? The precise localization of vibrotactile stimuli is crucial for applications that are aimed at conveying vibrotactile information. In order to evaluate the ability to distinguish between vibrotactile stimuli based on their frequency and location on the forearm, we used a relative point localization method. Participants were presented with pairs of sequential vibrotactile stimuli at three possible locations on the forearm and asked to determine whether the second stimulation occurred at the same location as the first one in the pair or not. The stimulation frequency varied between 100 Hz, 150 Hz, 200 Hz and 250 Hz, which covers the range of frequencies that human observers are most sensitive to. The amplitude was kept constant. Our results revealed that the ability to discriminate between actuators remained unaffected by variations in the frequency of vibrotactile stimulation within the tested frequency range. The accuracy of the tactile discrimination task was heavily dependent on the location of the stimulation on the forearm, with the highest accuracy close to the wrist and elbow, locations that may serve as tactile anchor points. Our results highlight the critical role of stimulation location in precise vibrotactile localization and the importance of careful consideration of location in the design of forearm-mounted vibrotactile devices.

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person’s intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1–20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time–frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

The sound amplification ratios of sealed air chambers with different shapes were quantitatively compared to design a body-conduction microphone to measure animal scratching sounds. Recently, quantitative monitoring of scratching intensity in dogs has been required. We have already developed a collar with a body-conduction microphone to measure body-conducted scratching sounds. However, the air chamber, one of the components of the body-conduction microphone, has not been appropriately designed. This study compared the amplification ratios of air chambers with different shapes through numerical analysis and experiments. According to the results, the horn-shaped air chamber achieved the highest amplification performance, at least for sound frequencies below 3 kHz. The simulated amplification ratio of the horn-shaped air chamber with a 1 mm height and a 15 mm diameter was 52.5 dB. The deformation of the bottom of the air chamber affected the amplification ratio. Adjusting the margin of the margined horn shape could maintain its amplification ratio at any pressing force. The simulated and experimental amplification ratios of the margined horn-shaped air chamber were 53.4 dB and 19.4 dB, respectively.

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.

Parkinson’s disease (PD) has become the second most common neurodegenerative condition following Alzheimer’s disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson’s disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients’ quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients’ quality of life.

IoT-based wearable devices are significantly contributing to personalized and pervasive modern healthcare. Traditional healthcare systems are handicapped by several factors, including shortages of physicians, nurses, healthcare devices, hospital beds, healthcare cost, etc. Conventional therapy is carried out either at a hospital or at home by certified therapists which is not affordable for many in developing nations. In this research work, we present IoT-based monitoring and an evaluation of key parameters and indicators of a wearable device used during the rehabilitation process in stroke patients’ hand therapy which can be operated by the users at home without the need for therapists. Sensors along with a controller board are used for signal acquisition, processing, and monitoring. The efficiency of therapy can be increased through real-time follow-up and feedback from therapists. Three different control methods are proposed and studied: smartphone-based speech, smartphone-based touch, and Internet of Things (IoT)-based dashboard. In addition, four different architectures, including: therapist therapy wearable, dual therapy wearable, user therapy wearable, and multiuser therapy wearable architectures are discussed. A rehabilitation therapy-based case study using the proposed wearable device with multiple volunteers was conducted. Therapists can remotely operate the device and train the users. Users can benefit and save on costs without the need to visit hospitals or therapy centers or hire therapists. Based on the results of the experiments with volunteers, we can confidently say that the proposed IoT-based wearable device can enhance the quality of life and well-being of the users.

We examined the changes in variables that could be recorded on wearable devices during the early stages of acute myocardial infarction (AMI) in an animal model. Early diagnosis of AMI is important for prognosis; however, delayed diagnosis is common because of patient hesitation and lack of timely evaluations. Wearable devices are becoming increasingly sophisticated in the ability to track indicators. In this study, we retrospectively reviewed the changes in four variables during AMI in a pig model to assess their ability to help predict AMI onset. AMI was created in 33 pigs by 90-min balloon occlusion of the left anterior descending artery. Blood pressure, EKG, and lactate and cardiac troponin I levels were recorded during the occlusion period. Blood pressure declined significantly within 15 min after balloon inflation (mean arterial pressure, from 61 ± 8 to 50 ± 8 mmHg) and remained at this low level. Within 5 min of balloon inflation, the EKG showed ST-elevation in precordial leads V1–V3. Blood lactate levels increased gradually after occlusion and peaked at 60 min (from 1.48 to 2.53 mmol/L). The continuous transdermal troponin sensor demonstrated a gradual increase in troponin levels over time. Our data suggest that significant changes in key indicators (blood pressure, EKG leads V1–V3, and lactate and troponin levels) occurred at the onset of AMI. Monitoring of these variables could be used to develop an algorithm and alert patients early at the onset of AMI with the help of a wearable device.

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment.

When it is necessary to detect various physiological signals of the human body, clothing embroidered with near-field effect patterns can be used as a long-term power supply medium to supply power to long-distance transmitters and receivers to form a wireless power supply system. The proposed system uses an optimized parallel circuit to achieve a power transfer efficiency of more than five times higher than that of the existing series circuit. The power transfer efficiency of simultaneously supplying energy to multiple sensors is increased higher than five times and even more when only one sensor is coupled. When powering eight sensors at the same time, the power transmission efficiency can reach 25.1%. Even when eight sensors powered by the coupled textile coils are reduced to one, the power transfer efficiency of the whole system can reach 13.21%. Additionally, the proposed system is also applicable when the number of sensors ranges from 2 to 12.

Passive shoulder supports show large potential for a wide range of applications, such as assisting activities of daily living and supporting work-related tasks. The rigid-link architecture used in currently available devices, however, may pose an obstacle to finding designs that offer low protrusion and close-to-body alignment. This study explores the use of mechanisms that employ a flexible element which connects the supported arm to an attachment at the back and acts as energy storage, transmission and part of the load bearing structure. Based on the synthesis method explained in this paper, a large scope investigation into possible flexure-based mechanism topologies is conducted. Thereby, many potential designs are discovered, which are presented, categorized and compared. Two promising designs are developed into prototypes, and are built and tested on a dedicated test bench. These two mechanisms reduce the necessary moment to lift the arm by more than 80 % throughout 85 % of the range of motion, while staying within 18 cm and 10 cm distance from the body, respectively. The study indicates that, due to its lower protrusion and interface loads, a design with a tapered flexure connecting the upper arm via a hinge to a spring-loaded slider at the back offers the most promising solution.

We proposed a wearable drone controller with hand gesture recognition and vibrotactile feedback. The intended hand motions of the user are sensed by an inertial measurement unit (IMU) placed on the back of the hand, and the signals are analyzed and classified using machine learning models. The recognized hand gestures control the drone, and the obstacle information in the heading direction of the drone is fed back to the user by activating the vibration motor attached to the wrist. Simulation experiments for drone operation were performed, and the participants’ subjective evaluations regarding the controller’s convenience and effectiveness were investigated. Finally, experiments with a real drone were conducted and discussed to validate the proposed controller.

Due to a reduction in reaction time and, consequently, the driver’s concentration, driving when fatigued has become an issue throughout time. Consequently, the likelihood of having an accident and it being fatal increases. In this work, we aim to identify an automatic method capable of detecting drowsiness transitions by considering the time, frequency, and nonlinear domains of heart rate variability. Therefore, the methodology proposed considers the multivariate statistical process control, using principal components analysis, with accelerometer and time, frequency, and nonlinear domains of the heart rate variability extracted by a wearable device. Applying the proposed approach, it was possible to improve the results achieved in the previous studies, where it was able to remove points out-of-control due to signal noise, identify the drowsy transitions, and, consequently, improve the drowsiness classification. It is important to note that the out-of-control points of the heart rate variability are not influenced by external noise. In terms of limitations, this method was not able to detect all drowsiness transitions, and in some individuals, it falls far short of expectations. Regarding this, is essential to understand if there is any pattern or similarity among the participants in which it fails., The project is funded by the “NORTE-01-0247-FEDER-0039720”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). It was also supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020., The authors would like to thank everyone who participated in the driving simulations and for the conditions available at the Polytechnic Institute of Cávado and Ave, 4750-810, Barcelos. This work was done in co-promotion between Optimizer-Lda, IPCA, LIACC and ISCCI.

The present study describes the development of a wearable device designed to assist those who work in an unstructured posture. In the manufacturing sector, industrial accidents have been steadily on the rise due to poor work environments and excessive workloads imposed on workers. Against this backdrop, the present study aimed to analyze various types of work, especially those performed in unstructured postures by heavy industry workers, who are frequently exposed to high workloads and poor work environments. Based on the analysis results, an attempt was made to develop a shoulder muscle-assistive wearable device capable of assisting a wearer who is working using their shoulder muscles. Various types of unstructured posture work are performed in heavy industries, including activities such as the welding and grinding of ship components and plant structures. They are typically conducted in narrow spaces with limited postures, causing many workers to suffer muscle fatigue. In the present study, as the first step of developing a shoulder muscle-assistive wearable device, different working scenarios were simulated, and the corresponding motion data and required torque values were estimated using motion capture devices. The obtained motion data and required torque values were reflected in the design of the wearable device. The main structural body of the shoulder muscle-assistive wearable device was made of a carbon fiber-reinforced composite to be lightweight. This shoulder muscle-assistive wearable device was designed to fully cover the range of motion for workers working in unstructured postures while generating the torque required for a given job, thereby enhancing the muscular endurance of the workers. The gravity compensation module of the designed shoulder muscle-assistive wearable device generates a support force of 4.47 Nm per shoulder. The shoulder muscle assistive wearable device was developed to provide support for approximately 30% of the shoulder joint’s maximum torque generated in overhead tasks. This shoulder muscle-assistive wearable device is expected to contribute to improving the productivity of field workers, while reducing the occurrence of musculoskeletal injuries arising from the aging of the working-age population.

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

In this work, the novel design of a sliding mode TriboElectric Nano Generator (TENG)—which can utilize vibration amplitude of a few hundred microns to generate useful electric power—is proposed for the first time. Innovative design features include motion modification to amplify relative displacement of the TENG electrodes and use of biological material-based micron-sized powder at one of the electrodes to increase power output. The sliding mode TENG is designed and fabricated with use of polyurethane foam charged with the biological material micropowder and PolyTetraFluoroEthylene (PTFE) strips as the electrodes. Experimentations on the prototype within frequency range of 0.5–6 Hz ensured peak power density of 0.262 mW/m2, corresponding to the TENG electrode size. Further numerical simulation is performed with the theoretical model to investigate the influence of various design parameters on the electric power generated by the TENG. Lastly, application of the proposed TENG is demonstrated in a wearable device as an in-shoe sensor. Conceptual arrangement of the proposed in-shoe sensor is presented, and numerical simulations are performed to demonstrate that the real size application can deliver peak power density of 0.747 mW/m2 and TENG; the voltage will accurately represent foot vertical force for various foot force patterns.

There are numerous communication barriers between people with and without hearing impairments. Writing and sign language are the most common modes of communication. However, written communication takes a long time. Furthermore, because sign language is difficult to learn, few people understand it. It is difficult to communicate between hearing-impaired people and hearing people because of these issues. In this research, we built the Sign-Glove system to recognize sign language, a device that combines a bend sensor and WonderSense (an inertial sensor node). The bending sensor was used to recognize the hand shape, and WonderSense was used to recognize the hand motion. The system collects a more comprehensive sign language feature. Following that, we built a weighted DTW fusion multi-sensor. This algorithm helps us to combine the shape and movement of the hand to recognize sign language. The weight assignment takes into account the feature contributions of the sensors to further improve the recognition rate. In addition, a set of interfaces was created to display the meaning of sign language words. The experiment chose twenty sign language words that are essential for hearing-impaired people in critical situations. The accuracy and recognition rate of the system were also assessed.

the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid, and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of aerosol jet printing (AJP). The developed biosensors were combined with a paper-based microfluidic system and a chitosanbased stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11 +/- 2 mM for the static setup and of 15 +/- 6 mM for the dynamic setup, while sensitivity is 0.02 and 0.04 mu A/mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in vivo validation for the future development of stand-alone wearable patches for noninvasive lactate measurements.

Sensing systems are becoming less and less invasive. In this context, flexible materials offer new opportunities that are impossible to achieve with bulky and rigid chips. Standard silicon sensors cannot be adapted to curved shapes and are susceptible to big deformations, thus discouraging their use in wearable applications. Another step forward toward minimising the impacts of the sensors can be to avoid the use of cables and connectors by exploiting wireless transmissions at ultra-high frequencies (UHFs). Thin-film bulk acoustic wave resonators (FBARs) represent the most promising choice among all of the piezoelectric microelectromechanical system (MEMS) resonators for the climbing of radio frequencies. Accordingly, the fabrication of FBARs on flexible and wearable substrates represents a strategic step toward obtaining a new generation of highly sensitive wireless sensors. In this work, we propose the design and fabrication of a flexible gravimetric sensor based on an FBAR on a polymeric substrate. The resonator presents one of the highest electromechanical coupling factors in the category of flexible AlN-based FBARs, equal to 6%. Moreover, thanks to the polymeric support layer, the presence of membranes can be avoided, which leads to a faster and cheaper fabrication process and higher robustness of the structure. The mass sensitivity of the device was evaluated, obtaining a promising value of 23.31 ppm/pg. We strongly believe that these results can pave the way to a new class of wearable MEMS sensors that exploit ultra-high-frequency (UHF) transmissions.

Phase 2 studies showed that focal seizures could be detected by algorithms using heart rate variability (HRV) in patients with marked autonomic ictal changes. However, wearable surface electrocardiographic (ECG) devices use electrode patches that need to be changed often and may cause skin irritation. We report the first study of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM; Confirm Rx, Abbott). For this proof-of-concept (phase 1) study, we recruited six patients admitted to long-term video-electroencephalographic monitoring. Fifteen-minute epochs of ECG signals were saved for each seizure and for control (nonseizure) epochs in the epilepsy monitoring unit (EMU) and in the patients' home environment (1–8 months). We analyzed the ICM signals offline, using a previously developed HRV algorithm. Thirteen seizures were recorded in the EMU, and 41 seizures were recorded in the home-monitoring period. The algorithm accurately identified 50 of 54 focal seizures (sensitivity = 92.6%, 95% confidence interval [CI] = 85.6%–99.6%). Twelve of the 13 seizures in the EMU were detected (sensitivity = 92.3%, 95% CI = 77.2%–100%), and 38 of the 41 seizures in the out-of-hospital setting were detected (sensitivity = 92.7%, 95% CI = 84.7%–100%). Four false detections were found in the 141 control (nonseizure) epochs (false alarm rate = 2.7/24 h). Our results suggest that automated seizure detection using a long-term, subcutaneous ICM device is feasible and accurate in patients with focal seizures and autonomic ictal changes.

This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum–Welch algorithm and to obtain the state transition probability matrix (Formula presented.) and the observation probability matrix (Formula presented.). Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work.

Background The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Introduction. Liver transplantation restores patients' physical and social life, and its quality. The prevalence of low physical activity in liver recipients is unknown as well as the impact of late liver allograft dysfunction on it. Liver transplantation enhances patient's return to the usual physical and social activity and improves the quality of life. However, the prevalence of low physical activity among liver recipients and the impact of the late allograft dysfunction on it, which is a risk factor for obesity and cardiovascular diseases, require studying.The aim of the study was to identify whether the late liver allograft dysfunction influences the physical activity of recipients.Material and methods. The study included 87 liver recipients. We measured anthropometric parameters, physical performance (SPPB, LFI, 6-min walk test), mean step count per day. Late liver allograft dysfunction was determined if elevated transaminases and/or cholestatic enzymes or hepatic failure have been diagnosed later than 3 months posttransplant. Activity trackers were provided to assess physical activity.Results. Median age was 54 years [45;61], 33% were men. The median follow-up period was 36 months [16;64]. The median of the average steps count was 5.9 [4.1;8.7] thousand per day. 60.5% of recipients were sedentary and low active, 24.4% were somewhat active, 15.1% were active. In cases of liver allograft dysfunction, the mean step count was significantly lower than in patients with normal liver function: 4.1 thousand [2.6;5.3] versus 6.8 thousand [4.2;9.4], p=0.003, despite no differences in the physical activity test results.Conclusion. In case of a late liver allograft dysfunction, the physical activity can decrease; 60.5% of liver recipients, in the absence of pathological restriction of movement, have a sedentary and low active lifestyle. Activity trackers may allow identifying patients who need additional check-up or physical training.

An ultra-sensitive capture of NO2 molecules and fast charge collection and transfer has been realized by constructing the model of artificial neuron-likegas sensing structure based on CuS quantum dots (QDs)/Bi2S3 nanosheets (NSs)realizes.Simulation analysis revealed that CuS QDs and Bi2S3NSs can be used, respectively, as the main adsorption sites and charge transport pathways, thus leading to a greatly enhanced gas capture ability and charge conduction performance of NO2. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00740-1., Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-021-00740-1.

In wearable or implantable biomedical devices that typically rely on battery power for diagnostics or operation, the development of flexible piezoelectric nanogenerators (NGs) that enable mechanical-to-electrical energy harvesting is finding promising applications. Here, we present the construction of a flexible piezoelectric nanogenerator using a thin film of room temperature deposited nanocrystalline aluminium nitride (AlN). On a thin layer of aluminium (Al), the AlN thin film was grown using pulsed laser deposition (PLD). The room temperature grown AlN film was composed of crystalline columnar grains oriented in the (100)-direction, as revealed in images from transmission electron microscopy (TEM) and X-ray diffraction (XRD). Fundamental characterization of the AlN thin film by piezoresponse force microscopy (PFM) indicated that its electro-mechanical energy conversion metrics were comparable to those of c-axis oriented AlN and zinc oxide (ZnO) thin films. Additionally, the AlN-based flexible piezoelectric NG was encapsulated in polyimide to further strengthen its mechanical robustness and protect it from some corrosive chemicals.

Resumen En este artículo proponemos un nuevo método para el cálculo de la simetría para la resistencia sensible a la fuerza (FSR) en dispositivos portátiles. El problema en este dominio es que solo se utilizan características discretas como la longitud de la zancada, la duración de la zancada o la duración de las fases de la marcha para el cálculo de la simetría. Sin embargo, esto puede conducir a fallas, ya que el uso de funciones puede resultar en una pérdida parcial de información de la serie temporal. A partir de esto, presentamos la posibilidad de calcular la simetría utilizando el método de Dynamic Time Warping (DTW). El DTW utiliza la serie de tiempo completa para el análisis y, por lo tanto, es independiente de ciertas características. Abstract In this paper we propose a new method for symmetry calculation in wearable devices. The problem in this domain is that only discrete features such as stride length, stride duration, or duration of gait phases are used for the symmetry calculation. However, this can lead to failures, since the use of features can result in partial loss of information from the time series. From this we present a possibility to calculate the symmetry by using Dynamic Time Warping (DTW). DTW uses the complete time series for the analysis and is therefore independent of certain features. Resumo Neste artigo propomos um novo método para cálculo de simetria para resistência sensível à força (FSR) em dispositivos portáteis. O problema neste domínio é que apenas são utilizadas características discretas, como comprimento da passada, duração da passada ou duração da fase da marcha para o cálculo de simetria. No entanto, isso pode levar a falhas, pois o uso de funções pode resultar em perda parcial de informações da série temporal. A partir disso, apresentamos a possibilidade de calcular a simetria utilizando o método Dynamic Time Warping (DTW). O DTW usa toda a série temporal para a análise e, portanto, é independente de determinados recursos.

Older adults walk with a shorter stride length, reduced hip range of motion (ROM) and higher cadence. These are signs of reductions in walking ability. This study investigated whether using a wireless smart insole system that monitored and provided biofeedback to encourage an extension of swing time could increase stride length and hip flexion, while reducing the cadence. Seven older adults were tested in this study, with and without the biofeedback device, in an outdoor environment. Gait analysis was performed by using GaitRite system and Xsens MVN. Repeated measures analysis demonstrated that with biofeedback, the swing time increased by 6.45%, stride length by 4.52% and hip flexion by 14.73%, with statistical significance. It also decreased the cadence significantly by 5.5%. This study has demonstrated that this smart insole system modified positively the studied gait parameters in older adults and has the potential to improve their walking ability.

BACKGROUND: The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily. OBJECTIVE: In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way. METHOD: Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents’ vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson. RESULTS: The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person’s health to improve sports person performance reliably.

Tactile sensations are crucial for achieving precise operations. A haptic connection between a human operator and a robot has the potential to promote smooth human-robot collaboration (HRC). In this study, we assemble a bilaterally shared haptic system for grasping operations, such as both hands of humans using a bottle cap-opening task. A robot arm controls the grasping force according to the tactile information from the human that opens the cap with a finger-attached acceleration sensor. Then, the grasping force of the robot arm is fed back to the human using a wearable squeezing display. Three experiments are conducted: measurement of the just noticeable difference in the tactile display, a collaborative task with different bottles under two conditions, with and without tactile feedback, including psychological evaluations using a questionnaire, and a collaborative task under an explicit strategy. The results obtained showed that the tactile feedback provided the confidence that the cooperative robot was adjusting its action and improved the stability of the task with the explicit strategy. The results indicate the effectiveness of the tactile feedback and the requirement for an explicit strategy of operators, providing insight into the design of an HRC with bilaterally shared haptic perception.

Purpose This study aimed to develop and evaluate the feasibility and preliminary efficiency of a methodology to measure the mindfulness state using a wearable device (“Cap”) capable of monitoring students’ levels of full attention by means of real-time measured heart rate variability (HRV). Methods The device was developed to export the data to the user’s smartphone via Bluetooth, which in turn stores the securely accessible data in the cloud. The autonomous wearable device consists of electronic boards of the Arduino platform that detect the period in milliseconds between two subsequent referential R peaks of the QRS complex wave through infrared oxygenation sensor. Results In a population of 13 subjects (8 female, 5 male, age 16.1 years \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm 0.58$$\end{document}±0.58), the Z-test (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.05$$\end{document}p

Heatstroke is a concern during sudden heat waves. We designed and prototyped an Internet of Things system for heatstroke prevention, which integrates physiological information, including deep body temperature (DBT), based on the dual-heat-flux method. A dual-heat-flux thermometer developed to monitor DBT in real-time was also evaluated. Real-time readings from the thermometer are stored on a cloud platform and processed by a decision rule, which can alert the user to heatstroke. Although the validation of the system is ongoing, its feasibility is demonstrated in a preliminary experiment.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.

Wearable devices are widely spreading in various scenarios for monitoring different parameters related to human and recently plant health. In the context of precision agriculture, wearables have proven to be a valuable alternative to traditional measurement methods for quantitatively monitoring plant development. This study proposed a multi-sensor wearable platform for monitoring the growth of plant organs (i.e., stem and fruit) and microclimate (i.e., environmental temperature—T and relative humidity—RH). The platform consists of a custom flexible strain sensor for monitoring growth when mounted on a plant and a commercial sensing unit for monitoring T and RH values of the plant surrounding. A different shape was conferred to the strain sensor according to the plant organs to be engineered. A dumbbell shape was chosen for the stem while a ring shape for the fruit. A metrological characterization was carried out to investigate the strain sensitivity of the proposed flexible sensors and then preliminary tests were performed in both indoor and outdoor scenarios to assess the platform performance. The promising results suggest that the proposed system can be considered one of the first attempts to design wearable and portable systems tailored to the specific plant organ with the potential to be used for future applications in the coming era of digital farms and precision agriculture.

In this paper we propose a new method for symmetry calculation in wearable devices. The problem in this domain is that only discrete features such as stride length, stride duration, or duration of gait phases are used for the symmetry calculation. However, this can lead to failures, since the use of features can result in partial loss of information from the time series. From this we present a possibility to calculate the symmetry by using Dynamic Time Warping (DTW). DTW uses the complete time series for the analysis and is therefore independent of certain features. En este artículo proponemos un nuevo método para el cálculo de la simetría para la resistencia sensible a la fuerza (FSR) en dispositivos portátiles. El problema en este dominio es que solo se utilizan características discretas como la longitud de la zancada, la duración de la zancada o la duración de las fases de la marcha para el cálculo de la simetría. Sin embargo, esto puede conducir a fallas, ya que el uso de funciones puede resultar en una pérdida parcial de información de la serie temporal. A partir de esto, presentamos la posibilidad de calcular la simetría utilizando el método de Dynamic Time Warping (DTW). El DTW utiliza la serie de tiempo completa para el análisis y, por lo tanto, es independiente de ciertas características. Neste artigo propomos um novo método para cálculo de simetria para resistência sensível à força (FSR) em dispositivos portáteis. O problema neste domínio é que apenas são utilizadas características discretas, como comprimento da passada, duração da passada ou duração da fase da marcha para o cálculo de simetria. No entanto, isso pode levar a falhas, pois o uso de funções pode resultar em perda parcial de informações da série temporal. A partir disso, apresentamos a possibilidade de calcular a simetria utilizando o método Dynamic Time Warping (DTW). O DTW usa toda a série temporal para a análise e, portanto, é independente de determinados recursos.

In recent years, Wearable Devices have been used in a wide variety of applications and fields, but because they span so many different disciplines, it is difficult to ascertain the intellectual structure of this entire research domain. No review encompasses the whole research domain related to Wearable Devices. In this study, we collected articles on wearable devices from 2001 to 2022 and quantitatively organized them by bibliometric analysis to clarify the intellectual structure of this research domain as a whole. The cluster analysis, co-occurrence analysis, and network centrality analysis were conducted on articles collected from the Web of Science. As a result, we identified one cluster that represents applied research and two clusters that represent basic research in this research domain. Furthermore, focusing on the top two countries contributing to this research domain, China and the USA., it was confirmed that China is extremely inclined toward basic research and the USA. toward applied research, indicating that applied and basic research are in balance. The basic intellectual structure of this cross-sectional research domain was identified. The results summarize the current state of research related to Wearable Devices and provide insight into trends.

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes.

Wearable devices are a new class of healthcare monitoring devices designed for use in close contact with the patient’s body. Such devices must be flexible to follow the contours of human anatomy. With numerous potential applications, a wide variety of flexible wearable devices have been created, taking various forms and functions. Therefore, different fabrication techniques and materials are employed, resulting in fragmentation of the list of equipment and materials needed to make different devices. This study attempted to simplify and streamline the fabrication process of all key components, including microfluidic chip and flexible electrode units. A combination of diode laser CNC machine and polyimide tape is used to fabricate flexible microfluidic chip and laser-induced graphene (LIG) electrodes, to create flexible microfluidic sensing devices. Laser ablation on polyimide tape can directly create microfluidic features on either PDMS substrates or LIG electrodes. The two components can be assembled to form a flexible microfluidic sensing device that can perform basic electrochemical analysis and conform to curved surfaces while undergoing microfluidic flow. This study has shown that simple, commonly available equipment and materials can be used to fabricate flexible microfluidic sensing devices quickly and easily, which is highly suitable for rapid prototyping of wearable devices.

Scoping review protocol

Postoperative delirium after spinal surgery in elderly patients has been a recent concern. However, there has not been a study of delirium after spinal surgery based on electroencephalography (EEG) signals from a compact wearable device. We aimed to analyze differences in EEG signals from a wearable device in patients with and without delirium after spinal surgery. Thirty-seven patients who underwent cervical or lumbar decompression and instrumented fusion for degenerative spinal disease were included. EEG waves were collected from a compact wearable device, and percentage changes from baseline to within 1 week and 3 months after surgery were compared between patients with and without delirium. In patients with delirium, the anxiety- and stress-related EEG waves—including the H-beta (19.3%; p = 0.003) and gamma (18.8%; p = 0.006) waves—and the tension index (7.8%; p = 0.011) increased, and the relaxation-related theta waves (−23.2%; p = 0.016) decreased within 1 week after surgery compared to the non-delirium group. These results will contribute to understanding of the EEG patterns of postoperative delirium and can be applied for the early detection and prompt treatment of postoperative delirium after spinal surgery.

Currently, portable X-ray fluorescence (PXRF) analysis is widely used as an auxiliary method for the preliminary investigation of soil heavy metal contamination. In this study, a smart glasses-based application (app) was developed to support field workers performing soil contamination surveys with a PXRF analyzer. The app was developed using the MIT App Inventor and runs on smart glasses based on an optical head-mounted display that provides both the original function of glasses to see the objects in front of the wearer, and the function of a computer at the same time. Using the app, a field worker wearing smart glasses can move to soil sampling points while checking the satellite image, survey plan, and real-time locations of other field workers through the smart glasses. At a sampling point, the worker can use both hands to collect and pretreat soil samples, and then measure the content of elements using a PXRF analyzer. The measurement results can be entered into the app using a wearable keyboard and shared in real-time with other field workers. The demonstration at the Ilgwang mine in Korea revealed that the app could effectively support field workers and shorten the working time compared to a previous study that was performed under the same conditions. The subjective workload was evaluated using the NASA task load index on ten subjects, and most of workload factors were evaluated as low.

EPSRC DTP; Department of Mechanical and Aerospace Engineering, Brunel University London.

For digging out eco−friendly and well−performed energy harvesters, piezoelectric nanogenerators are preferred owing to their effortless assembly. Corona−poling promotes output performance of either aligned or porous PVDF electrospun films and higher piezoelectric output was achieved by corona−poled porous PVDF electrospun films due to more poled electret dipoles in pores. Increasing the duration of electrospinning rendered more electret dipoles in PVDF porous electrospun films, resulting in higher piezoelectric output. Moreover, corona−poled PVDF/Y−ZnO porous electrospun films performed better than corona−poled PVDF/ZnO porous electrospun films because of the larger polar crystal face of Y−ZnO. Flexible piezoelectric polymer PVDF and high−piezoelectric Y−ZnO complement each other in electrospun films. With 15 wt% of Y−ZnO, corona−poled PVDF/Y−ZnO porous electrospun films generated maximum power density of 3.6 μW/cm2, which is 18 times that of PVDF/BiCl3 electrospun films.

Background Wearables and their benefits for the safety and well-being of users have been widely studied and have had an enormous impact on the general development of these kinds of devices. Yet, the extent of research into the use and impact of wearable devices in the underwater environment is comparatively low. In the past 15 years, there has been an increased interest in research into wearables that are used underwater, as the use of such wearables has steadily grown over time. However, there has so far been no clear indication in the literature about the direction in which efforts for the design and construction of underwater wearable devices are developing. Therefore, the analysis presented in this scoping review establishes a good and powerful basis for the further development and orientation of current underwater wearables within the field. Objective In this scoping review, we targeted wearable devices for underwater use to make a comprehensive map of their capabilities and features and discuss the general direction of the development of underwater wearables and the orientation of research into novel prototypes of these kinds of devices. Methods In September 2021, we conducted an extensive search for existing literature on 4 databases and for grey literature to identify developed prototypes and early-stage products that were described and tested in water, could be worn and interacted with (eg, displays, buttons, etc), and were fully functional without external equipment. The studies were written in English, came from peer-reviewed academic sources, and were published between 2005 and 2021. We reviewed each title and abstract. The data extraction process was carried out by one author and verified by another author. Results In total, 36 relevant studies were included. Among these, 4 different categories were identified; 18 studies dealt primarily with safety devices, 9 dealt with underwater communication devices, 7 dealt with head-up displays, and 2 dealt with underwater human-computer interaction approaches. Although the safety devices seemed to have gained the most interest at the time of this study, a clear trend toward underwater communication wearables was identified. Conclusions This review sought to provide a first insight into the possibilities and challenges of the technologies that have been used in and for wearable devices that are meant for use in the underwater environment. Among these, underwater communication technologies have had the most significant influence on future developments. Moreover, a topic that has not received enough attention but should be further addressed is human-computer interaction. By developing underwater wearables that cover 2 or more of the technology categories that we identified, the extent of the benefits of such devices can be significantly increased in the future.

The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly

A self-driven sensor that can detect urine and urine sugar and can be mounted on diapers is desirable to reduce the burden of long-term care. In this study, we created a paper-based glucose biofuel cell that can be mounted on diapers to detect urine sugar. Electrodes for biofuel cells were produced by printing MgO-templated porous carbon on which poly(glycidyl methacrylate) was modified using graft polymerization. A new bioanode was prepared through covalently modifying flavin-adenine-dinucleotide-dependent glucose dehydrogenase and azure A with pendant glycidyl groups of poly(glycidyl methacrylate). We prepared a cathode with covalently bonded bilirubin oxidase. Covalent bonding of enzymes and mediators to both the bioanode and biocathode suppressed elution and improved stability. The biofuel cell could achieve a maximum output density of 0.12 mW cm–2, and by combining it with a wireless transmission device, the concentration of glucose sensed from the transmission frequency was in the range of 0–10 mM. The sensitivity of the sensor was estimated at 0.0030 ± 0.0002 Hz mmol–1 dm3. This device is expected to be a new urine-sugar detection device, composed only of organic materials with a low environmental load and it can be useful for detecting postprandial hyperglycemia.

Objective To explore the value of smart glasses in monitoring the visual behavior of school-age children. Methods A total of 46 myopic children aged 9 to 11 years who visited our hospital during April to August 2019 were included in this study. Questionnaires were used to record the participants' daily outdoor time, near-work time, and parental supervision to obtain the baseline value of children's visual behavior. Besides, Akeso smart glasses were used to objectively record the daily wearing time, outdoor time, and near-work time of all participants from September 2019 to December 2019. Subsequently, we compared the differences in the children's visual behavior recorded by the questionnaire and Akeso smart glasses. In addition, we also compared the differences in the children's visual behavior on school days and weekends recorded by Akeso smart glasses, and analyzed the impact of parental supervision on the length of time the child wears glasses. Results The number of children corresponding to the different outdoor time recorded by the questionnaire was as follows: ≤30 min (n=5), >30 to 60 min (n=6), >60 to 120 min (n=23), >120 min (n=12), and this children's visual behavior parameter recorded by Akeso smart glasses was ≤30 min (n=16), >30 to 60 min (n=26), >60 to 120 min (n=4), >120 min (n=0). There were statistical differences between the results recorded by the above methods (P < 0.001). The results of Akeso smart glasses also showed significant differences were in the outdoor time spent on school days and weekends [24 (4, 56) vs 15 (3, 43) min/d, P < 0.001], and in the near-work time spent on school days and weekends (363.36±98.55 vs 329.08±105.90 min/d, P < 0.001). The children with parental supervision wore glasses for 711.80±101.63 min every day, while the wearing time for those without was 641.64±84.79 min, and there were statistical differences between these 2 groups (P=0.026). Conclusion Akeso smart glasses is able to objectively record children's visual behavior and can be used to supplement or replace questionnaires in the prevention and control myopia research.