From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.

Overview

1. Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
2. Artificial Intelligence (AI) Institute, Division of Health, Engineering, Computing and Science, University of Waikato, Hamilton, 3216 New Zealand
3. Assistive Intelligent Robotics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
4. Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany

Abstract

Despite substantial progress in decoding biosignals for human motion prediction, the influence of participant- and experiment-related factors on the decodability of these signals has received comparatively little attention. This study evaluates the continuous prediction of hand and wrist joint flexion using the MyoKi database, which comprises surface electromyography, inertial measurement units, and force myography data from 35 participants without disabilities performing 74 daily-life tasks. Unlike existing datasets, MyoKi includes tasks that mimic real-world scenarios by allowing natural movement variations and muscle fatigue. Using a long short-term memory neural network, the impact of participant- and experiment-related factors on decoding accuracy was investigated. Our results show that both expanding sensor coverage to additional muscle regions and combining multiple sensor modalities significantly improve decoding performance, with the greatest gains observed for joints controlled by extrinsic muscles. Muscle fatigue, recording time, and participant characteristics such as weight also influenced model accuracy. However, decoding of movements driven by intrinsic hand muscles remains challenging due to anatomical limitations. These findings highlight the importance of sensor placement and multimodal fusion for myoelectric decoding and provide guidance for optimizing sensor configurations in future prosthetic and robotic applications.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• figshare:28696778, at figshare; found in the references

Data availability

The MyoKi database analyzed within this work has been previously published and is available in the figshare repository (https://figshare.com/s/353a15d58e5d25db2359).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Funding: added Friedrich-Alexander-Universität Erlangen-Nürnberg

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 keywords, 12 MeSH terms, 70 references.

Cite

This paper

Andreas, D., Dwivedi, A., Castellini, C., & Beckerle, P. (2026). From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding. Scientific reports, 16(1), 20054. https://doi.org/10.1038/s41598-026-59979-6

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