Understanding knee mechanics and joint loading is vital for optimizing training and rehabilitation. This study analysed knee axis behaviour and lower limb muscle activity during a closed-chain exercise (Bilateral Squat, BLS), compared with an open-chain exercise (Seated Leg Extension, SLE) using time-series analysis of electromyographic (EMG) data and motion capture to assess knee axis inclination and translations through 90 degrees of flexion-extension. EMG data were filtered and normalized to maximum activity, and kinematic curves were analysed across the full movement cycle (0–100%) employing the Instantaneous Helical Axis (IHA) method and Statistical Parametric Mapping (SPM). IHA inclination and position were calculated across eccentric and concentric phases.Results demonstrated significant differences in IHA position and inclination between exercises, with exercise- and phase-dependent associations between IHA parameters and lower-limb muscle activity. IHA position was more anterior during BLS compared to SLE in both eccentric and concentric phases (>80%), whereas IHA inclination was greater during SLE, particularly during latter portions of the eccentric (60–100%) and concentric (85–100%) phases. Associations between IHA parameters, knee kinematics, and muscle activity also differed between exercises and movement phases.These findings indicate exercise- and phase-dependent differences in knee axis behaviour and muscle coordination despite comparable knee ranges of motion.
Surface electromyography (sEMG) is often used to estimate muscle activation for exercise selection, but session variability may limit absolute amplitude comparisons, while a rank-based approach may offer a more reliable alternative. This study examined the inter-day reliability of biceps brachii sEMG amplitude rank order across four arm curl exercises (concentration, dumbbell, hammer, and incline) in 15 adults (8 trained and 7 untrained, 37 ± 15 years) performing seven repetitions per exercise across four sessions at ~33% of peak isometric force. Amplitudes were normalized to single-peak isometric and dynamic (dumbbell curl) amplitudes.Exercise effects were significant for both methods, but session and interaction effects were not. Concentration produced significantly greater amplitudes than hammer, while isometric normalization was more reliable. Kendall's W showed a stable rank order across sessions (W = 0.90, permutation p = 0.0013), with hammer lowest, dumbbell/incline tied, and concentration first.Rank order was stable for exercise pairs with meaningful amplitude differences, but not for closely ranked exercises. However, one-third of participants showed only fair-to-moderate individual concordance across sessions. Although sEMG-based rank order can reliably distinguish exercises at the group level, individual-level reliability warrants verification before applying group findings to exercise prescription for an individual's goals at light-to-moderate loads.
Decomposition-based EMG studies have reported lower motor-unit (MU) discharge rates in diabetic peripheral neuropathy (DPN) than in controls at matched relative contraction intensities. Whether this reflects physiology or selective representation of active MUs remains unclear. We used a neuromuscular force-control model to simulate 50 paired subjects under a Normal and a simplified DPN-like condition. For each subject, the HD-sEMG-like estimate was the mean of 10 MUs drawn at random from those with discharge rates of 5–15 pps and an interspike-interval coefficient of variation ≤0.3; the known mean of all active MUs was the sole reference. At 20% MVC, the HD-sEMG-like estimate was lower in DPN (mean paired difference, DPN minus Normal: −0.48 pps; 95% BCa CI [−0.73, −0.25]; p < 0.001), whereas the complete active-MU population was higher in DPN (+1.03 pps; 95% BCa CI [0.93, 1.13]; p < 0.001), reflecting the lower twitch-to-tetanus ratio assigned to DPN MUs. The mean of all eligible MUs was also lower in DPN (−0.50 pps), so the reversal arose from eligibility, not from sample size. At 10% and 50% MVC, the HD-sEMG-like differences also remained negative (−0.27 and − 0.41 pps); the complete-population differences remained positive and varied with contraction intensity (p < 0.001). These simulations show that discharge-based MU eligibility criteria can reverse between-condition discharge-rate differences; therefore, lower rates in HD-sEMG-like samples do not necessarily indicate lower rates across the complete active-MU population.
Purpose. Currently, the clinical assessment of upper-limb motor function in stroke survivors relies heavily on subjective, time-consuming standardized scales, such as the Fugl-Meyer Assessment (FMA). To address this limitation, this study validates a simplified quantitative evaluation approach that integrates markerless motion capture with surface electromyography (sEMG) to assess motor recovery objectively. Methods. To evaluate this approach, 25 stroke patients were recruited to perform a targeted shoulder abduction task. During the task, a developed Markerless Upper Limb Motion Measurement System (MULMMS) synchronously captured kinematic data via Azure Kinect DK and sEMG signals from the Upper Trapezius and Middle Deltoid. Following trajectory smoothing via Singular Spectrum Analysis (SSA), a multidimensional feature set, including Maximum Joint Angle, smoothness metrics, and frequency-domain muscle activity, was extracted and validated against clinical scores using Spearman's correlation analysis. Results. Specific multimodal features demonstrated strong correlations with clinical scores. Maximum Joint Angle (Angmax) yielded the strongest kinematic correlation (rho = 0.932, p < 0.001). For electromyographic features, Median Frequency (rho = 0.806, p < 0.001) showed a highly significant correlation with motor recovery levels. Statistical analysis confirmed this single-task approach effectively distinguished patients across different clinical score stages (p < 0.001). Conclusion. The integration of markerless motion capture and sEMG provides an effective means to evaluate upper-limb motor impairment. Multimodal quantitative metrics based on a single core movement strongly correlate with traditional scales. Ultimately, this data-driven method for assessing shoulder motor compensation offers a standardized, simplified alternative that effectively complements subjective clinical observations.
