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

Abstract:A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user. A systematic review of 1077 studies quantifies where the evidence is thin: amputees appear in about one in six. Here a montage-agnostic cross-user encoder is carried to eleven transradial amputees on a protocol matched to its intact-limb training data. Zero-shot cross-population transfer fails outright: the encoder requires labeled data from the new user before it begins decoding, and it then exceeds the per-user classifier a clinic would fit by 0.190 macro F1 at three repetitions and for every subject in the cohort. Given three labelled repetitions it reaches 0.779 macro-F1 against 0.589 for the per-user pipeline. Training on forty intact subjects produces better transfers to a new amputee than training on ten other amputees, and combining the two produces better transfers than either individually. The prediction pre-registered for this study, which extends the encoder's baseline-strength account with the premise that amputee EMG is less separable, holds true only after a few repetitions become available and after enriching the source pool with additional amputees. At a single repetition, and at every budget under a source matched to the intact-limb comparison, it fails. Thus, it locates the boundary of the proposed account.

Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)

I.2.6; I.5.4; J.3

arXiv:2609.20297 [cs.LG]

(or arXiv:2609.20297v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.20297 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.