sEMG-Driven Robotic Manipulation in Simulation: LOPO Classification, Confidence-Gated Supervision, and Gesture-Scheduled LQR Control

Surface electromyography (sEMG) gesture classifiers consistently achieve high accuracy under random-split validation, yet performance degrades substantially under participant-independent evaluation, and classification accuracy alone does not establish effective robotic control. This study combines offline gesture recognition with confidence-gated finite-state supervision, minimum-jerk trajectory generation, and gesture-scheduled linear quadratic regulator (LQR) control of a simulated six-degrees-of-freedom manipulator. Four classifiers were evaluated: a backpropagation neural network (BPNN), generalized matrix learning vector quantization (GMLVQ), a hybrid BPNN–GMLVQ, and EMGTFNet, a fuzzy vision transformer-based network for sEMG. Each was benchmarked under 40-participant leave-one-participant-out (LOPO) cross-validation using a 116-dimensional feature vector that combines time-domain, spectral, and cross-correlation descriptors across eight upper-limb gesture classes. The confidence-gated classifier was a Mealy-type finite-state machine with model-specific thresholds and a dwell condition requiring three consecutive qualifying predictions. Each confirmed command was then mapped to a minimum-jerk joint-space trajectory, tracked by a gesture-indexed, kinematically informed LQR. Random-split accuracies exceeded 97% for all four classifiers, whereas the mean LOPO accuracies reached 85.38% for the hybrid model and 85.31% for EMGTFNet. Repeated-measures ANOVA and Friedman tests indicate significant differences among models, although these omnibus results do not establish individual pairwise superiority. Confidence gating reduced spurious state transitions from approximately 97% to zero, and gesture-indexed gain scheduling reduced the joint-space tracking error by 20–33% relative to a fixed global gain. Numerical transcription of the displayed simulation protocols yields 36 of 45 correct scheduled states for EMGTFNet and 74 of 90 for the hybrid model; these descriptive mapping outcomes are distinct from the classifier accuracy and transition success. Confidence thresholds were selected on LOPO outputs rather than on an independent inner partition, and end-to-end latency was not measured. The framework therefore provides a participant-independent classification benchmark and a simulation study of confidence-aware supervisory control, rather than evidence of physical deployment or real-time operation.