Interpretable side-aware kinematic-sEMG gait-state representations relevant to adaptive neurorobotic assistance after stroke: a public-dataset study.

Background: Adaptive lower-limb neurorobotics requires gaitd-state representations that preserve locomotor structure without reducing post-stroke walking to a single asymmetry score or opaque latent embedding. Because post-stroke gait is multimodal and side dependent, transparent side-aware representations may better support future adaptive-assistance design than modality-isolated summaries. Methods: This secondary analysis used a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The analytic space was restricted to 11 waveform domains shared across public exports: four sagittal kinematic waveforms and seven repository-normalized surface electromyography waveforms, each represented by 1,001 time-normalized points. Stroke waveforms were organized into paretic, non-paretic, bilateral-mean, and side-difference views, with side difference defined as paretic minus non-paretic. Domain-view functional principal component analysis retained 90% cumulative variance, capped at three components per block; family-level reduction retained 90% variance, capped at eight components. Candidate Ward hierarchical and K-means solutions from two to five states were screened in kinematics-only, sEMG-only, fused, paretic-only, and erector-spinae-excluded spaces. Results: The retained fused side-aware solution organized the strict complete-case stroke cohort ( Conclusion: Public waveform exports supported an internally interpretable, side-aware multimodal representation of post-stroke gait relevant to neurorobotic state-representation design. This contribution remains exploratory and representational, not clinical, interventional, real-time, or controller-validating; for future studies, it should be interpreted as a hypothesis-generating framework.