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arXiv Machine Learning By Jethro Odeyemi, W. J. Zhang

Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget

Read the original on arXiv Machine Learning →

The study evaluates how well a surface‑EMG gesture recogniser trained on intact‑limb data transfers to transradial amputees. Zero‑shot transfer fails; the model needs a few labelled repetitions from the new user to outperform a per‑user classifier, achieving a macro‑F1 of 0.779 versus 0.589. Training on a larger pool of intact subjects, or combining intact and amputee data, yields the best cross‑population performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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