Background: Spinal cord injury (SCI) disrupts brain–peripheral nervous system communication, causing total or partial loss of motor, sensory, and autonomic functions below the lesion. Although functional electrical stimulation (FES) cycling rehabilitation can improve cardiopulmonary function, muscle mass, and locomotion, its clinical adoption is hindered by complex parameter tuning and a lack of standardized and personalized protocols.

Objective: The goal of this work is to first develop a methodology for the creation of FES patterns from electromyography (EMG), specifically from muscle synergy analysis. Then, to study how the obtained patterns compare to a standard one. We hypothesized that (1) the standard FES pattern would diverge from healthy muscle-activation timing, (2) it was designed for intermediate cadences, and (3) synergy-based patterns would yield smoother pedaling and better user ratings.

Methods: During data acquisition, we recorded EMG of eight leg muscles from eight healthy controls and two SCI patients while cycling at 30, 50, and 70 revolutions per minute (rpm) on a recumbent ergometer. Non-negative matrix factorization extracted muscle synergies and reconstructed EMG envelopes, which defined personalized stimulation ranges and amplitude profiles for each muscle. We quantified inter-limb symmetry and deviations of the personalized pattern from the standard one to characterize how each FES parameter set differed across trials. In the evaluation phase, eight healthy participants tested three patterns in random order for six minutes: the standard pattern, a fixed-50-rpm synergy-based pattern, and a synergy-based pattern adapting to the real-time cadence. Outcome measures were rpm variability and user perception ratings.

Results: Two of our three hypotheses were confirmed: (1) the standard FES pattern diverged significantly from healthy muscle-activation timing, and (2) it appeared to have been indeed designed for mid-range cadences (50 rpm approx.). However, the synergy-based personalization did not yield clear improvements over the standard pattern, and subjective preferences did not consistently align with objective metrics. Since the statistical tests yielded no significant difference between trials, two illustrative cases were shown, highlighting variability.

Conclusion: A practical workflow is recommended to first generate candidate patterns from muscle synergy analysis, conduct brief comparative trials using application-specific measures and user feedback, and then select the pattern that best balances these criteria for the intended rehabilitation protocol.