Wearable Sensor System Research Articles

IoT Band: A Wearable Sensor System to Track Vital Data and Location of Missing or Earthquake Victims

Natural disasters, especially earthquakes, have caused and still cause serious loss of life in our country. Since many of our cities are located on fault lines, earthquake or collapse risks threaten our lives. In this study, a wearable sensor and tracking system has been developed to prevent or minimize the loss of life after a possible earthquake. The presented system consists of a wristband prototype designed to instantly monitor the vital data and location of victims trapped under the rubble. The wristband prototype includes a GPS module, a temperature sensor and a pulse oximeter. While the vital data of the victim is monitored by the temperature sensor and pulse oximeter, the location information of the victim is received via GPS. The data read from these sensors via a controller is transferred to a display screen through a wireless communication module. A computer and a mobile application were developed as the display screen. A Wi-Fi module was preferred for wireless communication. As an alternative to the Wi-Fi module, a GSM module was added to the wristband prototype. Thus, the order and time of rescue interventions for people trapped under the rubble can be determined. The presented work can be used not only for collapse and earthquake victims but also for Alzheimer's patients or people with poor mental development thanks to the GSM module. In this case, the patient's vital data and location will be transmitted to the user's relatives.

Synchronization of Packet Coupled Low-Accuracy RC Oscillator Clocks for Wireless Networks

Time-sensitive wireless applications have strict requirements on real-time data transmission and control operation. Even though time synchronisation has been extensively studied for providing a common timing among distributed wireless nodes, there still exists a lack of research for low-accuracy and large-drifting clocks, such as internal Resistor-Capacitor (RC) oscillator clocks with around 4 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> parts per million (ppm), which are widely used in wearable sensor systems. This paper proposes a Proportional Packet-Coupled Oscillators (P-PkCOs) protocol for synchronising poor-performing internal RC oscillator clocks with high disturbances in the single-cluster wireless network. The behaviour of such a drifting clock is described as a non-identical and time-varying model. To achieve time synchronisation on low-accuracy internal RC oscillator clocks, a packet-coupled synchronisation scheme is proposed for adjusting drifting clocks via the proportional control-based correction scheme. The RC oscillator frequency in an embedded system cannot be corrected, and this work utilises the clock threshold adjustment as a substitute for frequency correction. The stability region of controller parameters is given to guarantee that the clock threshold approaches a value, which is jointly determined by the nominal threshold and the corresponding clock frequency. We also propose a linear matrix inequality condition to prove that the P-PkCOs performance is robust against the large clock disturbances. We demonstrate the implementation of P-PkCOs. The experimental results show that P-PkCOs can achieve and maintain robust time synchronisation on the internal RC oscillator clocks.

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network.

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition

Chronic diseases are severe and life-threatening, and their accurate early diagnosis is difficult. Machine-learning-based processes of data collected from the human body using wearable sensors are a valid method currently usable for diagnosis. However, it is difficult for wearable sensor systems to obtain high-quality and large amounts of data to meet the demands of diagnostic accuracy. Furthermore, existing feature-learning methods do not deal with this problem well. To address the above issues, a sample-pair envelope diamond autoencoder ensemble algorithm (SP_DFsaeLA) is proposed. The proposed algorithm has four main components. Firstly, sample-pair envelope manifold neighborhood concatenation mechanism (SP_EMNCM) is designed to find pairs of samples that are close to each other in a manifold neighborhood. Secondly, the feature-embedding stacked sparse autoencoder (FESSAE) is designed to extend features. Thirdly, a staged feature reduction mechanism is designed to reduce redundancy in the extended features. Fourthly, the sample-pair-based model and single-sample-based model are combined by weighted fusion. The proposed algorithm was experimentally validated on nine datasets and compared with the latest algorithm. The experimental results show that the algorithm is significantly better than existing representative algorithms and it achieves the highest improvement of 22.77%, 21.03%, 24.5%, 27.89%, and 10.65% on five criteria over the state-of-the-art methods.

InMyFace: Inertial and mechanomyography-based sensor fusion for wearable facial activity recognition

Recognizing facial activity is a well-understood (but non-trivial) computer vision problem. However, reliable solutions require a camera with a good view of the face, which is often unavailable in wearable settings. Furthermore, in wearable applications, where systems accompany users throughout their daily activities, a permanently running camera can be problematic for privacy (and legal) reasons. This work presents an alternative solution based on the fusion of wearable inertial sensors, planar pressure sensors, and acoustic mechanomyography (muscle sounds). The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions. We present our integrated wearable sensor system, describe data fusion and analysis methods, and evaluate the system in an experiment with thirteen subjects from different cultural backgrounds (eight countries) and both sexes (six women and seven men). In a one-model-per-user scheme and using a late fusion approach, the system yielded an average F1 score of 85.00% for the case where all sensing modalities are combined. With a cross-user validation and a one-model-for-all-user scheme, an F1 score of 79.00% was obtained for thirteen participants (six females and seven males). Moreover, in a hybrid fusion (cross-user) approach and six classes, an average F1 score of 82.00% was obtained for eight users. The results are competitive with state-of-the-art non-camera-based solutions for a cross-user study. In addition, our unique set of participants and minimally biased experimental design demonstrate the inclusiveness of the approach, which is beneficial for further generalizability.

Passive and Wireless All-Textile Wearable Sensor System.

Mobile health technology and activity tracking with wearable sensors enable continuous unobtrusive monitoring of movement and biophysical parameters. Advancements in clothing-based wearable devices have employed textiles as transmission lines, communication hubs, and various sensing modalities; this area of research is moving towards complete integration of circuitry into textile components. A current limitation for motion tracking is the need forcommunication protocols demanding physical connection of textile with rigid devices,or vector network analyzers (VNA) with limited portability and lower sampling rates. Inductor-capacitor (LC) circuits are ideal candidates as textile sensors can be easily implemented with textile components and allow wireless communication. In this paper,the authors report a smart garment that can sense movement and wirelessly transmit data in real time. The garment features a passive LC sensor circuit constructed of electrified textile elements that sense strain and communicate through inductive coupling.Aportable, lightweight reader (fReader) is developed for achieving a faster sampling rate than a downsized VNA to track body movement, and for wirelessly reading sensor information suitable for deployment with a smartphone. The smart garment-fReader system monitors human movement in real-time and exemplifies the potential of textile-based electronics moving forward.

ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing): definition of possible use cases.

The ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing) project has received funding from EU's Horizon 2020 research and innovation program (grant agreement no. 825549). Its aim is to develop a wearable smart patch-based microneedle sensor system that can simultaneously measure several biomarkers in users' dermal interstitial fluid. This system could have several use cases based on continuous glucose and lactate monitoring: early detection of (pre-) diabetes mellitus, increasing physical performance through optimal carbohydrate intake, achieving a healthier lifestyle through behavioral changes based on the interpretation of glucose data, performance diagnostics (lactate threshold test), control of optimal training intensities corresponding with certain lactate levels, or warning of diseases/health threats, such as the metabolic syndrome or sepsis associated with increased lactate levels. The ELSAH patch system has a high potential of increasing health and wellbeing in users.

Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies.

For many human body diseases, treatments in the early stages are more efficient and safer than those in the later stages; therefore, detecting the early symptoms of a disease is crucial. One of the most significant early indicators for diseases is bio-mechanical motion. This paper provides a unique way of monitoring bio-mechanical eye motion based on electromagnetic sensing technology and a ferro-magnetic material, ferrofluid. The proposed monitoring method has the advantages of being inexpensive, non-invasive, sensor-invisible and extremely effective. Most of the medical devices are cumbersome and bulky, which makes them hard to apply for daily monitoring. However, the proposed eye-motion monitoring method is designed based on ferrofluid eye make-up and invisible sensors embedded inside the frame of glasses such that the system is wearable for daily monitoring. In addition, it has no influence on the appearance of the patient, which is beneficial for the mental health of some patients who do not want to attract public attention during treatment. The sensor responses are modelled using finite element simulation models, and wearable sensor systems are created. The designed frame of the glasses is manufactured based on 3-D printing technology. Experiments are conducted to monitor eye bio-mechanical motions, such as the frequency of eye blinking. Both the quick blinking behaviour with an overall frequency of around 1.1 Hz and the slow blinking behaviour with an overall frequency of around 0.4 Hz can be observed through experimentation. Simulations and measurements results show that the proposed sensor design can be employed for bio-mechanical eye-motion monitoring. In addition, the proposed system has the advantages of invisible sensor set-up and will not affect the appearance of the patient, which is not only convenient for the daily life of the patient but also beneficial for mental health.

Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review

Researchers are interested in measuring mental stress because it is linked to a variety of diseases. Real-time stress monitoring via wearable sensor systems can aid in the prevention of stress-related diseases by allowing stressors to be controlled immediately. Physical tests, such as heart rate or skin conductance, have recently been used to assess stress; however, these methods are easily influenced by daily life activities. As a result, for more accurate stress monitoring, validations requiring two or more stress-related biomarkers are demanded. In this review, the combinations of various types of sensors (hereafter referred to as multiplexed sensor systems) that can be applied to monitor stress are discussed, referring to physical and chemical biomarkers. Multiplexed sensor systems are classified as multiplexed physical sensors, multiplexed physical-chemical sensors, and multiplexed chemical sensors, with the effect of measuring multiple biomarkers and the ability to measure stress being the most important. The working principles of multiplexed sensor systems are subdivided, with advantages in measuring multiple biomarkers. Furthermore, stress-related chemical biomarkers are still limited to cortisol; however, we believe that by developing multiplexed sensor systems, it will be possible to explore new stress-related chemical biomarkers by confirming their correlations to cortisol. As a result, the potential for further development of multiplexed sensor systems, such as the development of wearable electronics for mental health management, is highlighted in this review.

Evaluation of a Wireless Home Sleep Monitoring System Compared to Polysomnography

Sleep is essential for human health. Bad sleep and sleep disorders have been increasingly prevalent and are gradually becoming a social problem that cannot be ignored. The current gold standard in sleep monitoring is polysomnography (PSG) allowing nearly complete approach. Unfortunately, this wealth of information is obtained at the cost of invasive system, only usable in hospital environment under the control of sleep experts. Therefore, the development of a wireless body network for long-term home sleep monitoring is a good way to achieve this in a less-intrusive, portable and autonomous way. In this paper, an overall architecture from the sensors to the user's display is presented with a focus on the main functions and hardware. The hardware architecture is composed of simple miniaturized wearable devices. Then, we introduce the chosen indicators for sleep monitoring and the algorithms developed for sleep stages classification. Finally we show the evaluation of our approach compared to the PSG. We illustrate the sleep stage classification during one night in the sleep unit of Toulouse University Hospital and highlight correlation between body temperature on extremities and Periodic Limb Movement during Sleep. Based on the confusion matrix analysis, the results show that the T1 method appears to be effective for the detection of awake and deep sleep in particular. For PLMS detection, we define the detection rules based on the foot movement data. The results show that the total number of PLMS and the number of PLMS distributed in each sleep stage detected by our foot module are both very close to the PSG. Furthermore, we have found correlations between body temperature and hypnogram and between body temperature on extremities and PLMS. A wearable sensor system could be an alternative to PSG for long-term monitoring. Validation of the two proposed threshold-based algorithmic methods for sleep stage classification compared to the PSG gold standard shows good agreement, while the k-means based approach shows poor agreement with PSG. Furthermore, this method could be a good candidate for predicting periodic leg movements in sleep.

Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal

Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties.In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction.Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue.The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.

Upper Body Pose Estimation Using a Visual–Inertial Sensor System With Automatic Sensor-to-Segment Calibration

Upper body kinematics is essential for motor function assessment and robot-assisted rehabilitation training. Wearable sensor systems, such as inertial measurement units (IMUs), provide affordable solutions to replace laboratory-based motion capture systems for use in daily life. However, the sensor-to-segment calibration often relies on predefined posture or movements, which is hard to perform accurately, particularly for patients with a limited range of motion. A visual–inertial sensor system is presented, which includes three sensor modules attached to the trunk, upper arm, and forearm. Each module has an IMU and an ArUco marker, which can be captured by a camera and the driftless orientation of the modules is computed from visual–inertial fusion. The sensor-to-segment transformations are calibrated from a period of arbitrary arm movements in either a 2-D plane or 3-D space, simulating the training process assisted by end-effector robots. Experiments were conducted to validate the feasibility and evaluate the accuracy of the proposed method. The estimated shoulder and elbow joint angles correlated well ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$ </tex-math></inline-formula> 0.986) with the ground truth from the optical motion capture (OMC) system. The joint angles presented low root-mean-square errors (RMSEs) ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 4^{\circ }$ </tex-math></inline-formula> ) except for the forearm pronation–supination angle (9.34°), which relied on manual alignment. The sensor system provides a simple and easy-to-use solution for movement assessment during robot-assisted training.

Estimation of Plate and Bowl Dimensions for Food Portion Size Assessment in a Wearable Sensor System.

Automatic food portion size estimation (FPSE) with minimal user burden is a challenging task. Most of the existing FPSE methods use fiducial markers and/or virtual models as dimensional references. An alternative approach is to estimate the dimensions of the eating containers prior to estimating the portion size. In this article, we propose a wearable sensor system (the automatic ingestion monitor integrated with a ranging sensor) and a related method for the estimation of dimensions of plates and bowls. The contributions of this study are: 1) the model eliminates the need for fiducial markers; 2) the camera system [automatic ingestion monitor version 2 (AIM-2)] is not restricted in terms of positioning relative to the food item; 3) our model accounts for radial lens distortion caused due to lens aberrations; 4) a ranging sensor directly gives the distance between the sensor and the eating surface; 5) the model is not restricted to circular plates; and 6) the proposed system implements a passive method that can be used for assessment of container dimensions with minimum user interaction. The error rates (mean ± std. dev) for dimension estimation were 2.01% ± 4.10% for plate widths/diameters, 2.75% ± 38.11% for bowl heights, and 4.58% ± 6.78% for bowl diameters.

The effect of wearable technology on psychomotor agitation in patients with diagnostic patients with schizophrenia expansion and psychosis

IntroductionDue to the exacerbation of psychotic processes in acute psychiatric services, patients may exhibit risky behaviors for themselves and others. Especially, psycho-motor agitation seen in schizophrenia may result in some harmful behaviors towards him/her self or other individuals in patient. Physical restrain, or chemical restrain with psychotropic drugs can be used for ensuring the safety of the patient with a tendency toward violent behavior and to prevent harm to himself and others. These restrain methods are usually applied when they showed aggressive or violent behaviors, that is after the observed warning signs or real violent behaviors.There is no system that can evaluate and notice agitation or tendency of violence before the obvious behaviors. By using a wearable sensor system to be able to measure some biological change and to evaluate of the sensors’ ability to obtain quantitative and objective data may help the health professionals to prevent the damage in advance.ObjectivesThe aim of this pilot study was to evaluate the changes in measurements of the four wearable sensors which applied to persons with schizophrenia.MethodsTen patients who restrained in the observation room, selected for this pilot study. On the first day (13:00), which meets the criteria for inclusion in the study and the end of the insulation process (the COVID test result is negative), the first measurement was before the noon treatment. The patients’ second measurements were taken on the day they switched from parenteral to oral treatment. For measurement, the sensor circuits developed at the Physiological Analysis and Wearable Systems Research Laboratory of Koç University were connected to various parts of the body to collect the non-invasive data detailed below. In addition, including the clinical status of the patient in the experimentation process, and a positive-negative syndrome scale was also used. The data from the patient was obtained under the supervision of the clinical chief nurse for 10 minutes. Sensors were electrocardiogram (ECG) photoplethysmogram (PPG), seismocardiography (SCG), body temperature.ResultsSince some recording errors observed in two patients’ records, the data of eight patients were evaluated. Aside from one of the eight patients evaluated, the signal deviation and strength of other patients’ data increased in general. This result imply that signal deviations and strengths may be reduced during the psychomotor agitation. These deviations may suggest that this sensor system is capable to evaluate some biological changes in patients.ConclusionsConsidering the results of the pilot study, it is planned to carry out future studies with a larger sample size and longer records. With these studies, it is thought that psychomotor agitation in patients can be determined in an objective and measurable way without risk.Disclosure of InterestNone Declared

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions.

Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions. This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject's movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54years old, with mean age 26.8, mean height 172.5cm and mean weight 66.9kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR. Statistically significant differences between dynamic activities ("walking slow", "walking fast", "running" and "cycling") and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy). Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

A Smart Tactile Sensing System Based on Carbon Nanotube/Polypropylene Composites for Wearable Applications

Recent advances in nanomaterials render the possibility of fabricating tactile sensors suitable for electronic skin, haptic interfaces, and biomedical applications. Furthermore, the problem of performing complex inertial measurements generated during the tactile process of polymer composites can be tackled using artificial intelligence (AI). However, the implementation of AI-based signal processing on embedded devices still represents an open area of opportunity for the design of the next generation of smart wearable sensor systems. A novel nanostructured smart tactile sensing system for wearable applications is proposed, using a 3-D-printed structure with embedded electronic devices. The highly sensitive piezoresistive tactile sensor is based on multiwall carbon nanotube/polypropylene (MWCNT/PP) composites. The integration of electronic circuits for signal processing of an artificial neural network (NN) on a digital controller unit improves the tactile interpretation in the wearable embedded device. Experiments show that the pressure classification results on MWCNT/PP composites with 98% accuracy.

A 3D Nanomaterials‐Printed Wearable, Battery‐Free, Biocompatible, Flexible, and Wireless pH Sensor System for Real‐Time Health Monitoring

AbstractThe pH sensing devices can provide important health information with applications in infection detection, disease diagnosis, and personalized medicine. However, these devices are often expensive with modest flexibility and require bulky readout instruments, thus inappropriate for wearable, remote, and continuous health monitoring applications. Herein, an integrated, miniaturized, modular, wearable, battery‐free, biocompatible, flexible, 3D‐printed (WB2F3D) sensor system for on‐demand, continuous, wireless, and real‐time pH monitoring is proposed, developed, and fully characterized. The 3D‐printing of nanomaterials on skin‐like flexible substrates is innovatively applied to enable multimaterial and multilayer printing of the sensors, reusable electronic/communication circuity, and antennas in a tailorable, low‐cost, and time‐efficient manner. The battery‐free and flexible readout system is designed to enable wireless and on‐demand energy and data transmission for continuous and real‐time pH monitoring. This sensor system exhibits high sensitivity (≈|51.76| mV pH−1), specificity, repeatability, reproducibility toward various pH ranges (3.0–10.0), excellent mechanical flexibility, and outstanding biocompatibility (cell viability &gt; = 90%). It successfully demonstrates the pH change monitoring in an ex situ hydrogel‐based wound model. The WB2F3D sensor system is envisioned to provide an integrated platform for accurate, on‐demand, battery‐free, wireless, and real‐time human health monitoring, and another step toward personalized medicine.

Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living.

Patients after stroke need to re-learn functional movements required for independent living throughout the rehabilitation process. In the study, we used a wearable sensory system for monitoring the movement of the upper limbs while performing activities of daily living. We implemented time-based and path-based segmentation of movement trajectories and muscle activity to quantify the activities of the unaffected and the affected upper limbs. While time-based segmentation splits the trajectory in quants of equal duration, path-based segmentation isolates completed movements. We analyzed the hand movement path and forearm muscle activity and introduced a bimanual movement parameter, which enables differentiation between unimanual and bimanual activities. The approach was validated in a study that included a healthy subject and seven patients after stroke with different levels of disabilities. Path-based segmentation provides a more detailed and comprehensive evaluation of upper limb activities, while time-based segmentation is more suitable for real-time assessment and providing feedback to patients. Bimanual movement parameter effectively differentiates between different levels of upper limb involvement and is a clear indicator of the activity of the affected limb relative to the unaffected limb.

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

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. This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. 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. 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%. 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.