Reproducible candidate kinematic-electromyographic waveform markers of post-stroke gait from public multimodal waveform exports.

Abstract

Background: Instrumented gait analysis after stroke is informative but difficult to scale for routine rehabilitation use. This secondary analysis examined whether a spreadsheet-restricted subset of public sagittal kinematic and surface electromyography (sEMG) waveforms could yield clinically legible candidate waveform-derived markers and reproducible subject-level summaries.

Methods: We analyzed a public multimodal gait dataset with 138 able-bodied adults and 50 adults with stroke. The workflow used only public spreadsheet exports and 11 domains shared across cohorts: four sagittal kinematic waveforms and seven normalized sEMG waveforms. Subject-level normative deviation and within-stroke asymmetry summaries were derived. Kinematics-only, sEMG-only, and combined kinematic-sEMG panels were compared using internal information-retention metrics within a nested benchmarking framework, not against an external clinical endpoint.

Results: All kinematic domains were complete in both cohorts. Seven-channel sEMG completeness was lower, yielding 102 able-bodied controls, 43 paretic stroke sides, 44 non-paretic stroke sides, and 43 paired stroke participants for sEMG-containing complete-case benchmarking. Combined-panel deviation burden remained non-trivial bilaterally, with the largest median domain-level abnormalities in gastrocnemius activity and knee-angle waveforms. Relative to the combined internal reference panel, kinematics-only and sEMG-only reductions preserved substantial ranking information in 43 paired complete cases, with Pearson correlations of 0.865 and 0.875 and Spearman correlations of 0.839 and 0.884, respectively. Bootstrap intervals and sensitivity analyses indicated overlap between reduced panels and strongest robustness for kinematics-only summaries.

Conclusion: Public spreadsheet waveform exports can support reproducible candidate gait markers, but reduced panels should be interpreted as internally benchmarked summaries rather than validated clinical biomarkers or prospective rehabilitation decision endpoints.