Insufficient sleep causes muscle fatigue, impacting performance. The mechanism of brain–muscle signaling remains uncertain. In this study, we examined the impact of sleep deprivation on muscle endurance during isometric contractions and explored the changes in brain–muscle connectivity. Methods: The research involved 35 right-handed male participants who took part in an exercise test that included isometric contractions of the left and right biceps in both sleep-deprived and well-rested states. Muscle contraction duration and electroencephalogram (EEG) and electromyography (EMG) signals were recorded. Functional connectivity between brain regions was assessed using the phase locking value (PLV), while partial directed coherence (PDC) was used to analyze signal directionality between motor centers and muscles. Results: The connectivity strength between Brodmann areas (BAs) 1-5 and the right BA6, 8 regions was significantly decreased in the isometric contractions after sleep deprivation. Insufficient sleep enhanced the PDC signals from the motor center of the right brain to the left biceps, and it decreased the PDC signals from both biceps to their opposite motor centers. Conclusions: Sleep deprivation shortened muscle isometric contraction duration by affecting the interaction between the somatosensory motor cortex and the right premotor cortex, reducing biceps feedback signal connectivity to the contralateral motor center in the brain.

Traditional assessment methods in physical education often emphasize final grades, lacking real-time monitoring and feedback during the learning process. To address this limitation and enhance the formative evaluation of student performance, this study proposes a real-time assessment system for Baduanjin instruction in physical education, utilizing a commercially available inertial measurement unit-based motion capture device. The system was developed in four stages. First, a dataset was created by recruiting 20 university students and one expert physical education instructor. Participants were asked to perform standardized Baduanjin routines while wearing wireless inertial measurement unit sensors on key body joints. The collected kinematic data, sampled at 100 Hz, included joint angles and movement trajectories. Second, preprocessing and feature extraction techniques were applied to the raw data to construct a labeled dataset for training. Third, supervised machine learning algorithms were used to build models for motion type recognition and motion accuracy evaluation. Model performance was assessed using cross-validation and compared with expert evaluations. Finally, a user-facing formative assessment system was developed and tested in a controlled classroom environment. The system demonstrated a high motion recognition accuracy of 99.77%, and the correlation coefficient between system-assessed motion accuracy and expert ratings exceeded 0.80, indicating strong validity. The results demonstrate that the formative assessment system built on inertial measurement unit is appropriate for the Baduanjin physical education.