Wearable Sensor System Research Articles

Analysis of time-space variations during dynamic cervical spine motion in cervical spondylosis myelopathy patients

BACKGROUND CONTEXTDecreased cervical range of motion (ROM) is a common symptom of myelopathy patients. Many previous studies have relied on a variety of experimental approaches for quantifying static cervical range of motion. However, the change rules of time-space variation during dynamic cervical spine motion remains unknown. PURPOSETo develop and validate the effectiveness of a novel wearable robot-based sensor system, Analysis of Dynamic Cervical spine Motion (ADCM), in evaluating the dynamic cervical spine motion dysfunction of patients with cervical spondylotic myelopathy (CSM). STUDY DESIGN/SETTINGA cross-sectional study. PATIENT SAMPLEOne hundred forty consecutive healthy individuals (70 men and 70 women) and 120 CSM patients (60 men and 60 women) were enrolled in the present study. OUTCOME MEASURESThe cervical motion process parameters, including the flexion and extension ROM, the flexion and extension time, and the Japanese Orthopedic Association scores (JOA) for cervical spine were measured. METHODSTwo hundred and sixty consecutive participants were asked to wear ADCM system and then fully flex and extend their neck rapidly and evenly at tolerable maximum speed. The cervical motion process was recorded and converted into waveforms. Relevant waveform parameters were measured and analyzed. The number of complete flexion-extension motions in 10 seconds has been defined as 10s F–E cycles. The Japanese Orthopedics Association (JOA) scores of CSM patients were marked. RESULTSCSM patients had a lower number of 10s F–E cycles than healthy subjects. There were significant differences in flexion and extension time and ROM between two groups. The waveforms of myelopathy patients were wider and lower than those in healthy individuals. The average ratio value (defined as F) of wave height to wave width (a+b/c+d) could quantitatively reflect such differences of waveforms. The average F value was correlated with the JOA scores of the cervical motion function (r=0.7538), and F value declined as JOA scores decreased. According to receiver operating characteristic curve analysis, the optimal threshold value of the normal average ratio was more than 34.7. CONCLUSIONSADCM appears to be an objective and quantitative severity assessment tool for confirmed CSM patients by evaluating dynamic cervical spine motion dysfunction.

Classification of Plank Techniques Using Wearable Sensors

The plank is a common core-stability exercise. Developing a wearable inertial sensor system for distinguishing between acceptable and aberrant plank techniques and detecting specific deviations from acceptable plank techniques can enhance performance and prevent injury. The purpose of this study was to develop an inertial measurement unit (IMU)-based plank technique quantification system. Nineteen healthy volunteers (age: 20.5 ± 0.8 years, BMI: 22.9 ± 1.4 kg/m2) performed the standard plank technique and six deviations with five IMUs positioned on the occiput, cervical spine, thoracic spine, sacrum, and right radius to record movements. The random forest method was employed to perform the classification. The proposed binary tree classification model achieved an accuracy of more than 86%. The average sensitivities were higher than 90%, and the specificities were higher than 91%, except for one deviation (83%). These results suggest that the five IMU-based systems can classify the plank technique as acceptable or aberrant with good accuracy, high sensitivity, and acceptable specificity, which has significant implications in monitoring plank biomechanics and enabling coaching practice.

A Liquid Metal-Enhanced Wearable Thermoelectric Generator.

It is a key challenge to continuously power personal wearable health monitoring systems. This paper reports a novel liquid metal-enhanced wearable thermoelectric generator (LM-WTEG that directly converts body heat into electricity for powering the wearable sensor system. The gallium-based liquid metal alloys with room-temperature melting point (24~30 °C) and high latent heat density (about 500 MJ/m3) are used to design a new flexible finned heat sink, which not only absorbs the heat through the solid-liquid phase change of the LM and enhances the heat release to the ambient air due to its high thermal conduction. The LM finned is integrated with WTEG to present high biaxial flexibility, which could be tightly in contact with the skin. The LM-WTEG could achieve a super high output power density of 275 μW/cm2 for the simulated heat source (37 °C) with the natural convective heat transfer condition. The energy management unit, the multi-parameter sensors (including temperature, humidity, and accelerometer), and Bluetooth module with a total energy consumption of about 65 μW are designed, which are fully powered from LM-WTEG through harvesting body heat.

IOT Based Covid-19 Patients Health Monitoring System

Currently, the COVID-19 pandemic is one of the major global issues faced by health organizations. As a person enters old age, it becomes increasingly vital for them to undergo standard medical health checkups. Since it may be time-consuming and difficult for most people to get regular health checkup appointments, IoT-based arrangements can be beneficial to individuals for routine health checkups. We are utilizing the internet of things to monitor several characteristics of the patient in this project. The real-time parameters of a patient’s health are transferred to the cloud via Internet connectivity in the patient Monitoring system based on the Internet of things project. These parameters are transmitted to IoT server where people can access them from any point on the planet. We demonstrate a multi-parameter Wearable sensor system that works in tandem with the Internet of Things to provide real-time, unobtrusive Monitoring of core body temperature and heart rate. Clinical study demonstrating the importance of sustaining precise measurements of core heartbeat and body temperature in the ambulatory environment and during Activity to examine human thermoregulation. We use a wireless multisensory system to monitor the temperature of the body as well as the pulse of the heart. The system’s goal is to build a health monitoring system that can quickly measure a variety of health factors

Patient and Implant Performance of Satisfied and Dissatisfied Total Knee Arthroplasty Patients.

Implant migration and altered kinematics have been thought to impact patient-reported outcome measures (PROMs) and postoperative patient satisfaction. In this study comparing satisfied and dissatisfied total knee arthroplasty (TKA) patients, we hypothesized that dissatisfied patients will have greater continuous implant migration and that there will be differences in joint kinematics, objective functional measurements, and PROMs between satisfied and dissatisfied patients. The Knee Society Score Satisfaction Subsection questions regarding satisfaction with function were used at least 6 months postoperation to split 50 patients into satisfied and dissatisfied groups. Patients underwent radiostereometric analysis to evaluate migration and kinematics. A wearable sensor system obtained objective measurements of patient function during timed up and go tests. PROMs were recorded preoperation and postoperation. No statisticallysignificant differences were found in migration between satisfied and dissatisfied groups. Statistical kinematic differences existed in lateral anteroposterior contact location at 20° and 40° of flexion at 1 year, where the dissatisfied group had more anteriorly located lateral contact. No statisticallysignificant differences were present in objective functional measurements. Satisfied and dissatisfied groups had differing PROMs at 4 timepoints or greater for each questionnaire. No differences were found in tibial component migration or objectively measured function between satisfied and dissatisfied patients. Functionally dissatisfied patients had more anteriorly positioned contact on the lateral condyle in early flexion and reported more pain and unmet expectations. These findings suggest that improving the functional satisfaction of TKA requires restoration of kinematics in early flexion and management of patient's pain and expectations.

Metal–organic frameworks-derived hollow octadecahedron nanocages for supercapacitors and wearable self-powered tactile stress sensor

A facile hollow octadecahedron nanocage structure derived by Co/Mn metal–organic framework is proposed by a controlled annealing method. Owing to the hybrid metal oxides in the peculiar polyhedral nanostructure, abundant active sites are exhibited on their surfaces, which carries high energy storage property with a capacitance of 948 F g−1 and achieves a high energy density of 32.90 W h kg−1 (250 W kg−1). Combining the nanocages with an elastic naturally-dried graphene aerogel in an asymmetric structure, a unique supercapacitive stress sensor is designed. Just relying the stored electric energy without extra power sources, the self-powered sensor can sensitively sense the stress of 0.2–10 N, by transforming mechanical signal to electrical signal in elastic-electrochemical mechanism. This offers a new opportunity for metal–organic frameworks derived nanoarchitecture and future applications in wearable elastic-electrochemical stress sensor systems.

Movement quantity and quality: How do they relate to pain and disability in dancers?

This field-based study aimed to determine the association between pre-professional student dancers' movement quantity and quality with (i) pain severity and (ii) pain related disability. Pre-professional female ballet and contemporary dance students (n = 52) participated in 4 time points of data collection over a 12-week university semester. At each time point dancers provided self-reported pain outcomes (Numerical Rating Scale as a measure of pain severity and Patient Specific Functional Scale as a measure of pain related disability) and wore a wearable sensor system. This system combined wearable sensors with previously developed machine learning models capable of capturing movement quantity and quality outcomes. A series of linear mixed models were applied to determine if there was an association between dancers' movement quantity and quality over the 4 time points with pain severity and pain related disability. Almost all dancers (n = 50) experienced pain, and half of the dancers experienced disabling pain (n = 26). Significant associations were evident for pain related disability and movement quantity and quality variables. Specifically, greater pain related disability was associated with more light activity, fewer leg lifts to the front, a shorter average duration of leg lifts to the front and fewer total leg lifts. Greater pain related disability was also associated with higher thigh elevation angles to the side. There was no evidence for associations between movement quantity and quality variables and pain severity. Despite a high prevalence of musculoskeletal pain, dancers' levels of pain severity and disability were generally low. Between-person level associations were identified between dancers' movement quantity and quality, and pain related disability. These findings may reflect dancers' adaptations to pain related disability, while they continue to dance. This proof-of-concept research provides a compelling model for future work exploring dancers' pain using field-based, serial data collection.

IOT Based Health Monitoring System for Covid Patients Monitoring

Currently, the COVID-19 pandemic is one of the major global issues faced by health organizations. As a person enters old age, it becomes increasingly vital for them to undergo standard medical health checkups. Since it may be time-consuming and difficult for most people to get regular health checkup appointments, IoT-based arrangements can be beneficial to individuals for routine health checkups. We are utilizing the internet of things to monitor several characteristics of the patient in this project. The real-time parameters of a patient’s health are transferred to the cloud via Internet connectivity in the patient Monitoring system based on the Internet of things project. These parameters are transmitted to IoT server where people can access them from any point on the planet. We demonstrate a multi-parameter Wearable sensor system that works in tandem with the Internet of Things to provide real-time, unobtrusive Monitoring of core body temperature and heart rate. Clinical study demonstrating the importance of sustaining precise measurements of core heartbeat and body temperature in the ambulatory environment and during Activity to examine human thermoregulation. We use a wireless multisensory system to monitor the temperature of the body as well as the pulse of the heart.

Effectiveness of Sensor-based Rehabilitation in Improving Outcomes in Patients Undergoing Total Knee Arthroplasty.

Physical rehabilitation after total knee arthroplasty (TKA) is important for long-term functional recovery. Recently, sensor-based home rehabilitation (SHR) has gained prominence as a promising method that allows monitoring and guidance that is both structured and accessible, compared to traditional methods of physical rehabilitation. Despite the advent of wearable sensor systems, there is a paucity of evidence regarding SHR in the current literature. Thus, this systematic review aimed to evaluate the effect of wearable SHR on post-TKA outcomes. We performed a systematic search of three electronic databases from the beginning of record to March 12, 2021. Primary outcomes were patient-reported outcome measures (PROMs) after rehabilitation, including the Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Knee Society Score (KSS). Secondary outcomes were physical activity levels and functional performance including range of motion (ROM) and Timed Up and Go Test (TUG). A total of 16 studies involving 1321 subjects were included. All wearable sensors in our included studies involved a combination of accelerometers, gyroscopes and magnetometers as functional units. These studies reported favourable outcomes for all three PROMs, although the extent of improvement in specific domains varied among studies. Moreover, physical activity in terms of daily steps and time spent on physical activity increased post-rehabilitation. Similarly, there were improvements in ROM and TUG that reflected a favourable post-operative trajectory during rehabilitation. SHR is effective for improving subjective and objective outcomes post-TKA. The role of SHR should be evaluated by a dedicated cost-benefit analysis to facilitate its wider adoption in healthcare systems.

System Performance and User Feedback Regarding Wearable Bioimpedance System for Multi-Site Knee Tissue Monitoring: Free-Living Pilot Study With Healthy Adults.

Knee-focused wearable devices have the potential to support personalized rehabilitation therapies by monitoring localized tissue alterations related to activities that reduce functional symptoms and pain. However, supporting these applications requires reported data to be reliable and accurate which can be challenging in the unsupervised free-living conditions that wearable devices are deployed. This pilot study has assessed a knee-focused wearable sensor system to quantify 1) system performance (operation, rates of data artifacts, environment impacts) to estimate realistic targets for reliable data with this system and 2) user experiences (comfort, fit, usability) to help inform future designs to increase usability and adoption of knee-focused wearables. Study data was collected from five healthy adult participants over 2 days, with 84.5 and 35.9% of artifact free data for longitudinal and transverse electrode configurations. Small to moderate positive correlations were also identified between changes in resistance, temperature, and humidity with respect to acceleration to highlight how this system can be used to explore relationships between knee tissues and environmental/activity context.

Ground Contact Time Estimating Wearable Sensor to Measure Spatio-Temporal Aspects of Gait.

Inpatient gait analysis is an essential part of rehabilitation for foot amputees and includes the ground contact time (GCT) difference of both legs as an essential component. Doctors communicate improvement advice to patients regarding their gait pattern based on a few steps taken at the doctor’s visit. A wearable sensor system, called Suralis, consisting of an inertial measurement unit (IMU) and a pressure measuring sock, including algorithms calculating GCT, is presented. Two data acquisitions were conducted to implement and validate initial contact (IC) and toe-off (TO) event detection algorithms as the basis for the GCT difference determination for able-bodied and prosthesis wearers. The results of the algorithms show a median GCT error of −51.7 ms (IMU) and 14.7 ms (sensor sock) compared to the ground truth and thus represent a suitable possibility for wearable gait analysis. The wearable system presented, therefore, enables a continuous feedback system for patients and, above all, a remote diagnosis of spatio-temporal aspects of gait behaviour based on reliable data collected in everyday life.

Increasing Nursing Documentation Efficiency With Wearable Sensors for Pressure Injury Prevention.

Documentation presents an overwhelming burden to bedside clinical nurses. Nurses must manually enter several hundred data points into electronic health record flow sheets, taking time from direct patient care and introducing opportunity for documentation errors. A patient record audit revealed a significant gap in documented patient repositioning events. This quality improvement initiative evaluated automated repositioning documentation via a wearable sensor system. A pretest-posttest design was used to examine retrospectively collected manual documentation and prospectively collected sensor documentation of patient repositioning events in a 148-bed rural community hospital. Repositioning documentation manually entered into electronic health records during the baseline period (January 1 to February 28, 2018) was compared with automatic, sensor-based repositioning documentation during the implementation period (corresponding months in 2019 and 2020 to eliminate seasonality). A convenience sample of 105 patient records was reviewed. The mean documented patient repositioning interval was 6.6 hours in the baseline period and 2.4 hours in the implementation period. The improvement was most pronounced in patients with obesity, whose mean repositioning interval improved from 9.4 hours to 2.5 hours. Documentation compliance (actual vs expected repositioning documentation) was 31% with manual documentation and 82% with automatic sensor-based documentation. Repositioning was documented more than 2.5 times as frequently with sensor technology as with manual data entry. Body position and reasons for delayed repositioning events were documented more completely with sensor technology. Automated documentation may improve the accuracy of electronic health records and reduce the documentation burden for nurses.

Wearable sensor system tracks lung function for COPD patients

Device combines a digital stethoscope, electrocardiogram monitor, thermometer, and goniometer into one wearable platform, which aims to predict COPD exacerbations for early intervention.

A high-performance wearable thermoelectric generator with comprehensive optimization of thermal resistance and voltage boosting conversion

This paper develops a superhigh-performance wearable thermoelectric generator (WTEG) for harvesting body heat, achieving an output power density of 15.8 μW/cm2 in windless and moveless conditions, and 97.6 μW/cm2 for walking of 0.8 m/s. A novel WTEG configuration integrated with the porous sandwich substrate and direct-soldering Cu-foam heat sink is designed to significantly improve its flexibility and considerably reduce the thermal resistance at the cold/hot sides. A new compact low-voltage boosting converter is optimized to obtain a high conversion efficiency (such as >50%@100 mV) and allow low self-startup input voltage (20 mV) and stable output voltage. A semi-automatization manufacturing process of the self-powered wearable sensor system is also designed to integrate the WTEG module, energy management module, multi-sensor (including acceleration, temperature, humidity, heart rate, and blood oxygen) module, and Bluetooth module onto the flexible substrate. The experimental results indicate that the sandwich substrate increases WTEG performance by 25% and achieves about 40 mW/cm2 for the constant temperatures at the cold/hot sides with the temperature difference of 60 °C. When the cold side of WTEG is exposed to the ambient air, the Cu-foam heat sink enables a performance increase of 73.6% compared to that without a heat sink for the ambient temperature of 18 °C, which even reaches 302% under the wind speed of 2 m/s (obtaining 457.97 μW/cm2). It is interesting to find that the bending WTEG could significantly increase its performance by 45.6% (for the curvature radius of 20 mm) compared to the plane case due to the thermal condition improvement. For harvesting body heat, the performance of WTEG fixed on the forehead is better than that on the arm or shank. It achieves a superhigh average output power of 3.12 mW for walking at the ambient temperature of 18 °C, which could fully power the wearable multi-sensor health monitoring system continuously.

A Wearable System for Jump Detection in Inline Figure Skating.

This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete’s back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment.

Hybrid deep convolutional model-based emotion recognition using multiple physiological signals

Emotion recognition has become increasingly utilized in the medical, advertising, and military domains. Recognizing the cues of emotion from human behaviors or physiological responses is encouraging for the research community. However, extracting true characteristics from sensor data to understand emotions can be challenging due to the complex nature of these signals. Therefore, advanced feature engineering techniques are required for accurate signal recognition. This study presents a hybrid affective model that employs a transfer learning approach for emotion classification using large-frame sensor signals which employ a genuine dataset of signal fusion gathered from 30 participants using wearable sensor systems interconnected with mobile devices. The proposed approach implements several learning algorithms such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and several other shallow methods on the sensor input to handle the requirements for the traditional feature extraction process. The findings reveal that the use of deep learning methods is satisfactory in affect recognition when a great number of frames is employed, and the proposed hybrid deep model outperforms traditional neural network (overall accuracy of 54%) and deep learning approaches (overall accuracy of 76%), with an average classification accuracy of 93%. This hybrid deep model also has a higher accuracy than our previously proposed statistical autoregressive hidden Markov model (AR-HMM) approach, with 88.6% accuracy. Accuracy assessment was performed by means of several statistics measures (accuracy, precision, recall, F-measure, and RMSE).

An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time.

Objective:Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson′s disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility.Methods:Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller.Results:We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment.Significance:The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.

Recent progress in self-powered multifunctional e-skin for advanced applications.

Electronic skin (e-skin), new generation of flexible wearable electronic devices, has characteristics including flexibility, thinness, biocompatibility with broad application prospects, and a crucial place in future wearable electronics. With the increasing demand for wearable sensor systems, the realization of multifunctional e-skin with low power consumption or even autonomous energy is urgently needed. The latest progress of multifunctional self-powered e-skin for applications in physiological health, human-machine interaction (HMI), virtual reality (VR), and artificial intelligence (AI) is presented here. Various energy conversion effects for the driving energy problem of multifunctional e-skin are summarized. An overview of various types of self-powered e-skins, including single-effect e-skins and multifunctional coupling-effects e-skin systems is provided, where the aspects of material preparation, device assembly, and output signal analysis of the self-powered multifunctional e-skin are described. In the end, the existing problems and prospects in this field are also discussed.

Integrated solid-state wearable sweat sensor system for sodium and potassium ion concentration detection

PurposeDesign, fabricate and evaluate all-solid-state wearable sensor systems that can monitor ion concentrations in human sweat to provide real time health analysis and disease diagnosis capabilities.Design/methodology/approachA human health monitoring system includes disposable customized flexible electrode array and a compact signal transmission-processing electronic unit.FindingsPatterned rGO (reduced-graphene oxide) layers can replace traditional metal electrodes for the fabrication of free-standing all solid film sensors to provide improved flexibility, sensitivity, selectivity, and stability in ion concentration monitoring. Electrochemical measurements show the open circuit potential of current selective electrodes exhibit near Nernst responses versus Na+ and K+ ion concentration in sweat. These signals show great stability during a typical measurement period of 3 weeks. Sensor performances evaluated through real time measurements on human subjects show strong correlations between subject activity and sweating levels, confirming high degree of robustness, sensitivity, reliability and practicality of current sensor systems.Originality/valueIn improving flexibility, stability and interfacial coherency of chemical sensor arrays, rGO films have been the developed as a high-performance alternative to conventional electrode with significant cost and processing complexity reduction. rGO supported solid state electrode arrays have been found to have linear potential response versus ion concentration, suitable for electrochemical sensing applications. Current sweat sensor system has a high degree of integration, including electrode arrays, signal processing circuits, and data visualization interfaces.

Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms.

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.