E-textile based modular sEMG suit for large area level of effort analysis.

We present a novel design for an e-textile based surface electromyography (sEMG) suit that incorporates stretchable conductive textiles as electrodes and interconnects within an athletic compression garment. The fabrication and assembly approach is a facile combination of laser cutting and heat-press lamination that provides for rapid prototyping of designs in a typical research environment without need for any specialized textile or garment manufacturing equipment. The materials used are robust to wear, resilient to the high strains encountered in clothing, and can be machine laundered. The suit produces sEMG signal quality comparable to conventional adhesive electrodes, but with improved comfort, longevity, and reusability. The embedded electronics provide signal conditioning, amplification, digitization, and processing power to convert the raw EMG signals to a level-of-effort estimation for flexion and extension of the elbow and knee joints. The approach we detail herein is also expected to be extensible to a variety of other electrophysiological sensors.

Monitoring at-home prosthesis control improvements through real-time data logging.

Objective.Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users.Approach.A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data.Main results.The participant's continuous prosthesis usage steadily increased (p= 0.04, max = 5.5 h) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time (p= 0.04), resulting in up to 5.4 h of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week,p< 0.001) with a maximum number of ten classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage (p< 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study.Significance. In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.

Longitudinal Tibia Stress Fracture Risk During High-Volume Training: A Multiscale Modeling Pipeline Incorporating Bone Remodeling.

Tibia stress fractures are prevalent during high-intensity training, yet a mechanistic model linking longitudinal training intensity, bone health, and long-term injury risk has yet to be demonstrated. The objective of this study was to develop and validate a multiscale model of gross and tissue level loading on the tibia including bone remodeling on a timescale of week. Peak tensile tibial strain (3517 µstrain) during 4 m/s running was below injury thresholds, and the peak anteromedial tibial strain (1248 µstrain) was 0.17 standard deviations away from the mean of reported literature values. An initial study isolated the effects of cortical density and stiffness on tibial strain during a simulated eight week training period. Tibial strains and cortical microcracking correlated with initial cortical modulus, with all simulations presenting peak anteromedial tensile strains (1047-1600 µstrain) near day 11. Average cortical densities decreased by 7-8% of their nominal value by day 11, but the overall density change was <2% by the end of the simulated training period, in line with reported results. This study demonstrates the benefits of multiscale models for investigating stress fracture risk and indicates that peak tibial strain, and thus injury risk, may increase early in a high intensity training program. Future studies could optimize training volume and recovery time to reduce injury risk during the most vulnerable training periods.

Applications of Pose Estimation in Human Health and Performance across the Lifespan.

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.