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.

Background: Gait and balance impairments are common in neurological diseases, including stroke, and negatively affect patients’ quality of life. Improving balance and gait are among the main goals of rehabilitation. Rehabilitation is mainly performed in clinics, which lack context specificity; therefore, training in the patient’s home environment is preferable. In the last decade, developed rehabilitation technologies such as virtual reality and augmented reality (AR) have enabled gait and balance training outside clinics. Here, we propose a new method for gait rehabilitation in persons who have had a stroke in which mobile AR technology and a sensor-based motion capture system are combined to provide fine-grained feedback on gait performance in real time. Objective: The aims of this study were (1) to investigate manipulation of the gait pattern of persons who have had a stroke based on virtual augmentation during overground walking compared to walking without AR performance feedback and (2) to investigate the usability of the AR system. Methods: We developed the ARISE (Augmented Reality for gait Impairments after StrokE) system, in which we combined a development version of HoloLens 2 smart glasses (Microsoft Corporation) with a sensor-based motion capture system. One patient with chronic minor gait impairment poststroke completed clinical gait assessments and an AR parkour course with patient-centered performance gait feedback. The movement kinematics during gait as well as the usability and safety of the system were evaluated. Results: The patient changed his gait pattern during AR parkour compared to the pattern observed during the clinical gait assessments. He recognized the virtual objects and ranked the usability of the ARISE system as excellent. In addition, the patient stated that the system would complement his standard gait therapy. Except for the symptom of exhilaration, no adverse events occurred. Conclusions: This project provided the first evidence of gait adaptation during overground walking based on real-time feedback through visual and auditory augmentation. The system has potential to provide gait and balance rehabilitation outside the clinic. This initial investigation of AR rehabilitation may aid the development and investigation of new gait and balance therapies.

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Gait and balance impairments are common in neurological diseases, including stroke, and negatively affect patients’ quality of life. Improving balance and gait are among the main goals of rehabilitation. Rehabilitation is mainly performed in clinics, which lack context specificity; therefore, training in the patient’s home environment is preferable. In the last decade, developed rehabilitation technologies such as virtual reality and augmented reality (AR) have enabled gait and balance training outside clinics. Here, we propose a new method for gait rehabilitation in persons who have had a stroke in which mobile AR technology and a sensor-based motion capture system are combined to provide fine-grained feedback on gait performance in real time. Objective: The aims of this study were (1) to investigate manipulation of the gait pattern of persons who have had a stroke based on virtual augmentation during overground walking compared to walking without AR performance feedback and (2) to investigate the usability of the AR system. Methods: We developed the ARISE (Augmented Reality for gait Impairments after StrokE) system, in which we combined a development version of HoloLens 2 smart glasses (Microsoft Corporation) with a sensor-based motion capture system. One patient with chronic minor gait impairment poststroke completed clinical gait assessments and an AR parkour course with patient-centered performance gait feedback. The movement kinematics during gait as well as the usability and safety of the system were evaluated. Results: The patient changed his gait pattern during AR parkour compared to the pattern observed during the clinical gait assessments. He recognized the virtual objects and ranked the usability of the ARISE system as excellent. In addition, the patient stated that the system would complement his standard gait therapy. Except for the symptom of exhilaration, no adverse events occurred. Conclusions: This project provided the first evidence of gait adaptation during overground walking based on real-time feedback through visual and auditory augmentation. The system has potential to provide gait and balance rehabilitation outside the clinic. This initial investigation of AR rehabilitation may aid the development and investigation of new gait and balance therapies.

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.

Purpose: Physical fatigue alters neuromuscular function and movement mechanics and is associated with reduced performance and increased injury risk. Wearable sensing technologies offer a potential means of discriminating fatigue outside the laboratory; however, most prior approaches rely on a single sensor modality, which may limit robustness across individuals and tasks. The purpose of this study was to evaluate the feasibility of classifying physical fatigue state using a fusion of physiological and biomechanical measurements during treadmill running.,Methods: Ten healthy, physically active adults completed baseline and fatigued running trials while joint kinematics, lower-extremity electromyography (EMG), and ground reaction forces were collected. Fatigue was induced using a standardized multi-stage protocol and defined using a combination of subjective exertion and performance decline. Features extracted from gait, kinematic, EMG, and force signals were down-selected using statistical filtering, correlation analysis, and recursive feature elimination. Classification models were evaluated using leave-one-participant-out cross-validation to assess subject-independent performance., Results: Feature selection reduced an initial set of 912 features to 15 features, primarily derived from EMG signals. Linear discriminant analysis and support vector machine classifiers achieved average cross-validation accuracies exceeding 80%. Across models, EMG-derived features consistently contributed most strongly to fatigue discrimination.,Conclusion: These findings demonstrate the feasibility of classifying physical fatigue state using multimodal wearable physiological and biomechanical data in a controlled laboratory setting. The results highlight the potential value of incorporating muscle activity measurements into future wearable fatigue-monitoring systems, while underscoring the need for larger-scale studies to evaluate generalizability across populations and movement tasks.