Low-Density sEMG Four-Finger Recalibration with Limited Calibration Data via Classwise Amplitude and Covariance Adaptation of Prior Training Data

Sensor re-donning can alter surface electromyography (sEMG) amplitudes and inter-channel relationships, degrading sEMG-based motion classifiers. This study proposes personalized recalibration for four-finger motion classifiers and evaluates whether reusing prior Training data can reduce newly acquired Calibration data. Limited labeled Calibration data transform Training signals toward the current condition via classwise amplitude correction and classwise regularized covariance alignment before support vector machine retraining. Three able-bodied male participants (P1–P3) were evaluated using Stable Motion window-level accuracy. For P1, accuracy pooled over ten Test datasets from five re-donning conditions increased from 88.90% to 98.88%. For P2 and P3, exploratory post hoc analyses of four failure-case directions per participant showed pooled accuracy increasing from 24.46% to 98.97% and from 73.74% to 96.72%, respectively. Across exhaustive retrospective analyses of all shared two-of-five repetition subsets, Combined accuracy was 98.29%, 98.68%, and 95.58% for P1–P3, respectively, only 0.18–0.58 percentage points below the five-repetition results; prospectively shortened Calibration recordings were not evaluated. These findings support the feasibility of participant-specific recalibration for Stable Motion four-finger classification after sensor re-donning and warrant further evaluation in broader user populations and continuous-operation settings.