Force Myography Shows Higher Correlation to Force Output Compared to ARV EMG Features

Sierotowicz M, Castellini C, Anam K (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

IEEE ... International Conference on Rehabilitation Robotics : [proceedings] Institute of Electrical and Electronics Engineers Inc.

Publisher: IEEE Computer Society

Pages Range: 642-647

Conference Proceedings Title: IEEE International Conference on Rehabilitation Robotics

Event location: Chicago, IL, USA

ISBN: 9798350380682

DOI: 10.1109/ICORR66766.2025.11062957

Abstract

Force myography, or FMG, is an emerging technology which, despite not quite yet achieving the same maturity as surface electromyography, still shows much promise for the purposes of muscle activity-based human-machine interfaces. Compared to surface electromyography, FMG setups can be very cost-effective and robust, and they offer the fundamental advantage of not requiring direct contact between sensor and skin. These factors make FMG sensors an attractive option for the purpose of controlling assistive wearable robotics or prostheses, for instance. Electromyographic features have been demonstrated to lead the force output by a sizable margin, which can be taken advantage of in various applications. However, FMG as a modality is inherently correlated with muscular force, we hypothesize that it should also exhibit a higher correlation with measurable force output of the extremities in healthy subjects compared to commonly used electromyographic features. In this study, we set out to prove this hypothesis by performing a regression analysis evaluating how well isometric pressure exerted by fingers can be predicted based on FMG signals as opposed to electromyographic signals. Additionally, we demonstrated the effectiveness of a system that is particularly low-cost, which in conjunction with the robustness of FMG sensors, would make them an attractive solution to be used in resource-constrained settings.

Authors with CRIS profile

Marek Sierotowicz Assistive Intelligent Robotics Lab Claudio Castellini Assistive Intelligent Robotics Lab

Involved external institutions

Universitas Jember ID Indonesia (ID)

How to cite

APA:

Sierotowicz, M., Castellini, C., & Anam, K. (2025). Force Myography Shows Higher Correlation to Force Output Compared to ARV EMG Features. In IEEE International Conference on Rehabilitation Robotics (pp. 642-647). Chicago, IL, USA: IEEE Computer Society.

MLA:

Sierotowicz, Marek, Claudio Castellini, and Khairul Anam. "Force Myography Shows Higher Correlation to Force Output Compared to ARV EMG Features." Proceedings of the 2025 International Conference on Rehabilitation Robotics, ICORR 2025, Chicago, IL, USA IEEE Computer Society, 2025. 642-647.

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