Recent Advances in Nanowire-Based Wearable Physical Sensors

Wearable electronics is a technology that closely integrates electronic devices with the human body or clothing, which can realize human-computer interaction, health monitoring, smart medical, and other functions. Wearable physical sensors are an important part of wearable electronics. They can sense various physical signals from the human body or the surrounding environment and convert them into electrical signals for processing and analysis. Nanowires (NW) have unique properties such as a high surface-to-volume ratio, high flexibility, high carrier mobility, a tunable bandgap, a large piezoresistive coefficient, and a strong light-matter interaction. They are one of the ideal candidates for the fabrication of wearable physical sensors with high sensitivity, fast response, and low power consumption. In this review, we summarize recent advances in various types of NW-based wearable physical sensors, specifically including mechanical, photoelectric, temperature, and multifunctional sensors. The discussion revolves around the structural design, sensing mechanisms, manufacture, and practical applications of these sensors, highlighting the positive role that NWs play in the sensing process. Finally, we present the conclusions with perspectives on current challenges and future opportunities in this field.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

Wearable sensing for understanding and influencing human movement in ecological contexts

Wearable sensors offer a unique opportunity to study movement in ecological contexts - that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.

Metallic Micro-Nano Network-Based Soft Transparent Electrodes: Materials, Processes, and Applications

Soft transparent electrodes (TEs) have received tremendous interest from academia and industry due to the rapid development of lightweight, transparent soft electronics. Metallic micro-nano networks (MMNNs) are a class of promising soft TEs that exhibit excellent optical and electrical properties, including low sheet resistance and high optical transmittance, as well as superior mechanical properties such as softness, robustness, and desirable stability. They are genuinely interesting alternatives to conventional conductive metal oxides, which are expensive to fabricate and have limited flexibility on soft surfaces. This review summarizes state-of-the-art research developments in MMNN-based soft TEs in terms of performance specifications, fabrication methods, and application areas. The review describes the implementation of MMNN-based soft TEs in optoelectronics, bioelectronics, tactile sensors, energy storage devices, and other applications. Finally, it presents a perspective on the technical difficulties and potential future possibilities for MMNN-based TE development.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Taste the emotions: pilot for a novel, sensors-based approach to emotional analysis during coffee tasting

BACKGROUND

Coffee is a natural drink with important properties for the human body and mind, capable of delivering energy and strong emotions, thus being appreciated since ancient times. The qualitative and quantitative assessment of the coffee properties is normally performed by trained panelists, though relying on standardized questionnaires, with possible biases arising. In this study, for the first time in the scientific literature, we applied a technology-based approach, based on the use of wearable sensors, to study the implicit emotional responses of a small cohort of experienced coffee judges, thus taking this chance to assess the feasibility of this approach in such a scenario. The merging of different technologies for capturing biomedical signals, including electrocardiogram, galvanic skin response, and electroencephalogram, was therefore adopted to retrieve results in terms of the relationships between implicit (i.e. psychophysiological) and explicit (i.e. derived from questionnaires) measurements.

RESULTS

Significant correlations were obtained between biomedical signals and data from the questionnaires within all the sensory domains (olfaction, vision, taste) investigated, particularly concerning autonomic-related features.

CONCLUSIONS

The results obtained confirmed the viability of this new approach in the psychophysical and emotional assessment in coffee tasting judges, paving the way for a new perspective into the universe of coffee quality assessment panels, eventually transferable to broader scale investigations, somewhat dealing with consumer satisfaction and neuromarketing at large. © 2023 Society of Chemical Industry.

Visible Light Communications-Based Assistance System for the Blind and Visually Impaired: Design, Implementation, and Intensive Experimental Evaluation in a Real-Life Situation

Severe visual impairment and blindness significantly affect a person's quality of life, leading sometimes to social anxiety. Nevertheless, instead of concentrating on a person's inability, we could focus on their capacities and on their other senses, which in many cases are more developed. On the other hand, the technical evolution that we are witnessing is able to provide practical means that can reduce the effects that blindness and severe visual impairment have on a person's life. In this context, this article proposes a novel wearable solution that has the potential to significantly improve blind person's quality of life by providing personal assistance with the help of Visible Light Communications (VLC) technology. To prevent the wearable device from drawing attention and to not further emphasize the user's deficiency, the prototype has been integrated into a smart backpack that has multiple functions, from localization to obstacle detection. To demonstrate the viability of the concept, the prototype has been evaluated in a complex scenario where it is used to receive the location of a certain object and to safely travel towards it. The experimental results have: i. confirmed the prototype's ability to receive data at a Bit-Error Rate (BER) lower than 10-7; ii. established the prototype's ability to provide support for a 3 m radius around a standard 65 × 65 cm luminaire; iii. demonstrated the concept's compatibility with light dimming in the 1-99% interval while maintaining the low BER; and, most importantly, iv. proved that the use of the concept can enable a person to obtain information and guidance, enabling safer and faster way of traveling to a certain unknown location. As far as we know, this work is the first one to report the implementation and the experimental evaluation of such a concept.

Corrigendum: Single- and dual-task gait performance and their diagnostic value in early-stage Parkinson's disease

[This corrects the article DOI: 10.3389/fneur.2022.974985.].

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category's cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

Uncovering Subtle Gait Deterioration in People with Early-Stage Multiple Sclerosis Using Inertial Sensors: A 2-Year Multicenter Longitudinal Study

Limited longitudinal studies have been conducted on gait impairment progression overtime in non-disabled people with multiple sclerosis (PwMS). Therefore, a deeper understanding of gait changes with the progression of the disease is essential. The objective of the present study was to describe changes in gait quality in PwMS with a disease duration ≤ 5 years, and to verify whether a change in gait quality is associated with a change in disability and perception of gait deterioration. We conducted a multicenter prospective cohort study. Fifty-six subjects were assessed at baseline (age: 38.2 ± 10.7 years, Expanded Disability Status Scale (EDSS): 1.5 ± 0.7 points) and after 2 years, participants performed the six-minute walk test (6MWT) wearing inertial sensors. Quality of gait (regularity, symmetry, and instability), disability (EDSS), and walking perception (multiple sclerosis walking scale-12, MSWS-12) were collected. We found no differences on EDSS, 6MWT, and MSWS-12 between baseline and follow-up. A statistically significant correlation between increased EDSS scores and increased gait instability was found in the antero-posterior (AP) direction (r = 0.34, p = 0.01). Seventeen subjects (30%) deteriorated (increase of at least 0.5 point at EDSS) over 2 years. A multivariate analysis on deteriorated PwMS showed that changes in gait instability medio-lateral (ML) and stride regularity, and changes in ML gait symmetry were significantly associated with changes in EDSS (F = 7.80 (3,13), p = 0.003, R2 = 0.56). Moreover, gait changes were associated with a decrease in PwMS perception on stability (p < 0.05). Instrumented assessment can detect subtle changes in gait stability, regularity, and symmetry not revealed during EDSS neurological assessment. Moreover, instrumented changes in gait quality impact on subjects' perception of gait during activities of daily living.

User Perspectives of Geriatric German Patients on Smart Sensor Technology in Healthcare

With consideration of the progressing aging of our societies, the introduction of smart sensor technology can contribute to the improvement of healthcare for older patients and to reductions of the costs of care. From the clinical and medico-ethical points of view, the advantages of smart sensor technology are copious. However, any ethical evaluation of an introduction of a new technology in medical practice requires an inclusion of patients' perspectives and their assessments. We have conducted qualitative, semi-structured, exploratory interviews with 11 older patients in order to gain their subjective opinions on the use of smart sensor devices for rehabilitation purposes. The interviews were analyzed using methods of qualitative content and thematic analyses. In our analysis, we have focused on ethical aspects of adoption of this technology in clinical practice. Most of the interviewees expressed their trust in this technology, foremost because of its accuracy. Several respondents stated apprehension that the use of smart sensors will lead to a change in the patient-healthcare professional relationship. Regarding costs of introduction of smart sensors into healthcare, interviewees were divided between health insurance bearing the costs and individual participation in corresponding costs. Most interviewees had no concerns about the protection of their privacy or personal information. Considering these results, improvement of users' technology literacy regarding possible threats connected with putting smart sensors into clinical practice is a precondition to any individual application of smart sensors. This should occur in the form of extended and well-designed patient information adapted to individual levels of understanding. Moreover, application of smart sensors needs to be accompanied with careful anamnesis of patient's needs, life goals, capabilities, and concerns.

Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

Accurate Monitoring of 24-h Real-World Movement Behavior in People with Cerebral Palsy Is Possible Using Multiple Wearable Sensors and Deep Learning

Monitoring and quantifying movement behavior is crucial for improving the health of individuals with cerebral palsy (CP). We have modeled and trained an image-based Convolutional Neural Network (CNN) to recognize specific movement classifiers relevant to individuals with CP. This study evaluates CNN's performance and determines the feasibility of 24-h recordings. Seven sensors provided accelerometer and gyroscope data from 14 typically developed adults during videotaped physical activity. The performance of the CNN was assessed against test data and human video annotation. For feasibility testing, one typically developed adult and one adult with CP wore sensors for 24 h. The CNN demonstrated exceptional performance against test data, with a mean accuracy of 99.7%. Its general true positives (TP) and true negatives (TN) were 1.00. Against human annotators, performance was high, with mean accuracy at 83.4%, TP 0.84, and TN 0.83. Twenty-four-hour recordings were successful without data loss or adverse events. Participants wore sensors for the full wear time, and the data output were credible. We conclude that monitoring real-world movement behavior in individuals with CP is possible with multiple wearable sensors and CNN. This is of great value for identifying functional decline and informing new interventions, leading to improved outcomes.

Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

A modular, deep learning-based holistic intent sensing system tested with Parkinson's disease patients and controls

People living with mobility-limiting conditions such as Parkinson's disease can struggle to physically complete intended tasks. Intent-sensing technology can measure and even predict these intended tasks, such that assistive technology could help a user to safely complete them. In prior research, algorithmic systems have been proposed, developed and tested for measuring user intent through a Probabilistic Sensor Network, allowing multiple sensors to be dynamically combined in a modular fashion. A time-segmented deep-learning system has also been presented to predict intent continuously. This study combines these principles, and so proposes, develops and tests a novel algorithm for multi-modal intent sensing, combining measurements from IMU sensors with those from a microphone and interpreting the outputs using time-segmented deep learning. It is tested on a new data set consisting of a mix of non-disabled control volunteers and participants with Parkinson's disease, and used to classify three activities of daily living as quickly and accurately as possible. Results showed intent could be determined with an accuracy of 97.4% within 0.5 s of inception of the idea to act, which subsequently improved monotonically to a maximum of 99.9918% over the course of the activity. This evidence supports the conclusion that intent sensing is viable as a potential input for assistive medical devices.

Nacre-Inspired MXene Nanocomposite-based Strain Sensor with Ultrahigh Sensitivity in a Small Strain Range for Parkinson's Disease Diagnosis

Nowadays, chronic diseases are the primary threat to public health and are getting younger. By taking the advantages of continuousness, convenience, and real-time response, wearable strain sensors have been given great attention to diagnose chronic diseases via analyzing the patient's health state. However, most physiological signals, such as limb tremor of Parkinson's disease, microexpression, and slight joint movement, are tiny and difficult to be detected. Therefore, the development of strain sensors characterized with ultrahigh sensitivity in a small strain range (ε < 10%) is urgent. Inspired by nacre's hierarchical structure, we have fabricated nacre-mimetic nanocomposites with "brick-and-mortar" architecture by employing polyacrylamide (PAM) and Ti3C2Tx MXene nanosheets through a layer-by-layer (LBL) spin-coating technique. The resultant nanocomposite-based strain sensor exhibits ultrahigh sensitivity in a small strain range (GF = 296.8, ε < 10%), attributed to the bioinspired hierarchical structure and hydrogen bond-enhanced interfacial interactions. In addition, a high reliability, broad working sensing range (453%), short response time (183 ms), skin-like tensile stress (7.2 MPa), and excellent durability (2000 cycles) are also achieved. Due to the ultrahigh sensitivity within a small strain, the reported strain sensor can accurately diagnose and distinguish Parkinson's disease symptoms, including thumb pill-rolling tremor, masked face (microexpression), intermittent shaking of the head, and limb cogwheel motion. This work provides new insights to design strain sensors with high sensitivity for monitoring tiny signals and for disease diagnosis.

Detecting early-stage Parkinson's disease from gait data

Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.

Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics

Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.

The Effect of a Smartphone App with an Accelerometer on the Physical Activity Behavior of Hospitalized Patients: A Randomized Controlled Trial

Inactive behavior is common in hospitalized patients. This study investigated the effectiveness of using a smartphone app with an accelerometer (Hospital Fit) in addition to usual care physiotherapy on increasing patients' physical activity (PA) behavior. A randomized controlled trial was performed at Maastricht University Medical Centre. Patients receiving physiotherapy while hospitalized at the department of Pulmonology or Internal Medicine were randomized to usual care physiotherapy or using Hospital Fit additionally. Daily time spent walking, standing, and upright (standing/walking) (min) and daily number of postural transitions were measured with an accelerometer between the first and last treatment. Multiple linear regression analysis was performed to determine the association between PA behavior and Hospital Fit use, corrected for functional independence (mILAS). Seventy-eight patients were included with a median (IQR) age of 63 (56-68) years. Although no significant effects were found, a trend was seen in favor of Hospital Fit. Effects increased with length of use. Corrected for functional independence, Hospital Fit use resulted in an average increase of 27.4 min (95% CI: -2.4-57.3) standing/walking on day five and 29.2 min (95% CI: -6.4-64.7) on day six compared to usual care. Hospital Fit appears valuable in increasing PA in functionally independent patients.

Changes in Electromyographic Activity of the Dominant Arm Muscles during Forehand Stroke Phases in Wheelchair Tennis

The aim of this study was to determine the muscle activations of the dominant arm during the forehand stroke of wheelchair tennis. Five players participated in the present study (age: 32.6 ± 9.9 years; body mass: 63.8 ± 3.12 kg; height: 164.4 ± 1.7 cm). The electrical muscle activity of six dominant arm muscles was recorded using an sEMG system. A significant effect of the muscle's activity was observed, and it was shown that the muscle activation was significantly higher in the execution phase compared to the preparation phase in the anterior deltoid and biceps brachii (34.98 ± 10.23% and 29.13 ± 8.27%, p < 0.001); the posterior deltoid, triceps brachii, flexor carpi radialis, and extensor carpi radialis were higher in the follow-through phase than in the execution phase (16.43 ± 11.72%, 16.96 ± 12.19%, 36.23 ± 21.47% and 19.13 ± 12.55%, p < 0.01). In conclusion, it was determined that the muscle activations of the dominant arm muscles demonstrate variances throughout the phases of the forehand stroke. Furthermore, the application of electromyographic analysis to the primary arm muscles has been beneficial in understanding the muscular activity of the shoulder, elbow, and wrist throughout the various phases of the forehand stroke in wheelchair tennis.

Verification and Validation of Lower Body Negative Pressure as a Non-Invasive Bioengineering Tool for Testing Technologies for Monitoring Human Hemorrhage

Since hemorrhage is a leading cause of preventable death in both civilian and military settings, the development of advanced decision support monitoring capabilities is necessary to promote improved clinical outcomes. The emergence of lower body negative pressure (LBNP) has provided a bioengineering technology for inducing progressive reductions in central blood volume shown to be accurate as a model for the study of the early compensatory stages of hemorrhage. In this context, the specific aim of this study was to provide for the first time a systematic technical evaluation to meet a commonly accepted engineering standard based on the FDA-recognized Standard for Assessing Credibility of Modeling through Verification and Validation (V&V) for Medical Devices (ASME standard V&V 40) specifically highlighting LBNP as a valuable resource for the safe study of hemorrhage physiology in humans. As an experimental tool, evidence is presented that LBNP is credible, repeatable, and validated as an analog for the study of human hemorrhage physiology compared to actual blood loss. The LBNP tool can promote the testing and development of advanced monitoring algorithms and evaluating wearable sensors with the goal of improving clinical outcomes during use in emergency medical settings.

Validation of Inertial Measurement Units for Analyzing Golf Swing Rotational Biomechanics

Training devices to enhance golf swing technique are increasingly in demand. Golf swing biomechanics are typically assessed in a laboratory setting and not readily accessible. Inertial measurement units (IMUs) offer improved access as they are wearable, cost-effective, and user-friendly. This study investigates the accuracy of IMU-based golf swing kinematics of upper torso and pelvic rotation compared to lab-based 3D motion capture. Thirty-six male and female professional and amateur golfers participated in the study, nine in each sub-group. Golf swing rotational kinematics, including upper torso and pelvic rotation, pelvic rotational velocity, S-factor (shoulder obliquity), O-factor (pelvic obliquity), and X-factor were compared. Strong positive correlations between IMU and 3D motion capture were found for all parameters; Intraclass Correlations ranged from 0.91 (95% confidence interval [CI]: 0.89, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation; Pearson coefficients ranged from 0.92 (95% CI: 0.92, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation (p < 0.001 for all). Bland-Altman analysis demonstrated good agreement between the two methods; absolute mean differences ranged from 0.61 to 1.67 degrees. Results suggest that IMUs provide a practical and viable alternative for golf swing analysis, offering golfers accessible and wearable biomechanical feedback to enhance performance. Furthermore, integrating IMUs into golf coaching can advance swing analysis and personalized training protocols. In conclusion, IMUs show significant promise as cost-effective and practical devices for golf swing analysis, benefiting golfers across all skill levels and providing benchmarks for training.

Friend or foe: classifying collaborative interactions using fNIRS

To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.