Jorge Ortiz Ceballos

Model

semg-intent-restoration-pipeline

Jorge Ortiz Ceballos

Three chained PyTorch models (plus one exploratory variant) implementing the Each 200 ms / 400-sample sEMG window (2000 Hz, single channel, MyoWare-compatible) is routed through: intent detection → quality assessment → (optional) restoration. Operating thresholds (τintent = 0.40, τquality = 0.50) were selected via grid search on the validation split only, and evaluated once on a held-out test split. If you want to reproduce the pipeline exactly as described in Section 4.7 and Figure 1 of the paper, use only the first three files. The task-aware variant is a separate experiment reported transparently as a limitation, not a replacement for restorerbest.pt — see the "Downstream Motor-Intent…