SnowMotion: A Wearable Sensor-Based Mobile Platform for Alpine Skiing Technique Assistance.

Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier's body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport.

Motion Capture Technology in Sports Scenarios: A Survey.

Motion capture technology plays a crucial role in optimizing athletes' skills, techniques, and strategies by providing detailed feedback on motion data. This article presents a comprehensive survey aimed at guiding researchers in selecting the most suitable motion capture technology for sports science investigations. By comparing and analyzing the characters and applications of different motion capture technologies in sports scenarios, it is observed that cinematography motion capture technology remains the gold standard in biomechanical analysis and continues to dominate sports research applications. Wearable sensor-based motion capture technology has gained significant traction in specialized areas such as winter sports, owing to its reliable system performance. Computer vision-based motion capture technology has made significant advancements in recognition accuracy and system reliability, enabling its application in various sports scenarios, from single-person technique analysis to multi-person tactical analysis. Moreover, the emerging field of multimodal motion capture technology, which harmonizes data from various sources with the integration of artificial intelligence, has proven to be a robust research method for complex scenarios. A comprehensive review of the literature from the past 10 years underscores the increasing significance of motion capture technology in sports, with a notable shift from laboratory research to practical training applications on sports fields. Future developments in this field should prioritize research and technological advancements that cater to practical sports scenarios, addressing challenges such as occlusion, outdoor capture, and real-time feedback.

Quantitative Assessment of Traumatic Upper-Limb Peripheral Nerve Injuries Using Surface Electromyography.

BACKGROUND: There is a great demand for convenient and quantitative assessment of upper-limb traumatic peripheral nerve injuries (PNIs) beyond their clinical routine. This would contribute to improved PNI management and rehabilitation. OBJECTIVE: The aim of this study was to develop a novel surface EMG examination method for quantitatively evaluating traumatic upper-limb PNIs. METHODS: Experiments were conducted to collect surface EMG data from forearm muscles on both sides of seven male subjects during their performance of eight designated hand and wrist motion tasks. All participants were clinically diagnosed as unilateral traumatic upper-limb PNIs on the ulnar nerve, median nerve, or radial nerve. Ten healthy control participants were also enrolled in the study. A novel framework consisting of two modules was also proposed for data analysis. One module was first used to identify whether a PNI occurs on a tested forearm using a machine learning algorithm by extracting and classifying features from surface EMG data. The second module was then used to quantitatively evaluate the degree of injury on three individual nerves on the examined arm. RESULTS: The evaluation scores yielded by the proposed method were highly consistent with the clinical assessment decisions for three nerves of all 34 examined arms (7 × 2 + 10 × 2), with a sensitivity of 81.82%, specificity of 98.90%, and significate linear correlation (p < 0.05) in quantitative decision points between the proposed method and the routine clinical approach. CONCLUSION: This study offers a useful tool for PNI assessment and helps to promote extensive clinical applications of surface EMG.

Surface Electromyographic Examination of Poststroke Neuromuscular Changes in Proximal and Distal Muscles Using Clustering Index Analysis.

Whether stroke-induced paretic muscle changes vary across different distal and proximal muscles remains unclear. The objective of this study was to compare paretic muscle changes between a relatively proximal muscle (the biceps brachii muscle) and two distal muscles (the first dorsal interosseous muscle and the abductor pollicis brevis muscle) following hemisphere stroke using clustering index (CI) analysis of surface electromyograms (EMGs). For each muscle, surface EMG signals were recorded from the paretic and contralateral sides of 12 stroke subjects versus the dominant side of eight control subjects during isometric muscle contractions to measure the consequence of graded levels of contraction (from a mild level to the maximal voluntary contraction). Across all examined muscles, it was found that partial paretic muscles had abnormally higher or lower CI values than those of the healthy control muscles, which exhibited a significantly larger variance in the CI via a series of homogeneity of variance tests (p < 0.05). This finding indicated that both neurogenic and myopathic changes were likely to take place in paretic muscles. When examining two distal muscles of individual stroke subjects, relatively consistent CI abnormalities (toward neuropathy or myopathy) were observed. By contrast, consistency in CI abnormalities were not found when comparing proximal and distal muscles, indicating differences in motor unit alternation between the proximal and distal muscles on the paretic sides of stroke survivors. Furthermore, CI abnormalities were also observed for all three muscles on the contralateral side. Our findings help elucidate the pathological mechanisms underlying stroke sequels, which might prove useful in developing improved stroke rehabilitation protocols.