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.
Related
- Toward process-level decoding of cognition with cross-domain transfer
- PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding
- Mangrove taxon mapping from Sentinel-1/2 using a compact one-dimensional network with probability calibration, predictive entropy, and frozen transfer evaluation
- Ten numbers from the Laplace-Beltrami spectrum facilitate training-free classification of protein structures based on surface shape
- Rhythms in Decoding Do Not Signal Pulsing Information
BibTeX
@misc{odeyemi2026intacttoamputee,
title = {{Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget}},
author = {Jethro Odeyemi and W. J. Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20297},
eprint = {2609.20297},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20297},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget
AU - Jethro Odeyemi
AU - W. J. Zhang
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.20297
UR - https://arxiv.org/abs/2609.20297
AB - 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.
ER -