MyoPose: position-limb-robust neuromechanical features for enhanced hand gesture recognition in colocated sEMG-pFMG armbands.

Journal: Journal of neural engineering
Published Date: Aug 6, 2025

Abstract

Surface electromyography (sEMG) and pressure-based force myography (pFMG) are two complementary modalities adopted in hand gesture recognition due to their ability to capture muscle electrical and mechanical activity, respectively. While sEMG carries rich neural information about the intended gestures and has long been established as the primary control signal in myoelectric interfaces, pFMG has recently emerged as a stable modality that is less sensitive to sweat and can indicate motion onset earlier than sEMG, making their fusion promising for robust pattern recognition. However, gesture classification systems based on these signals often suffer from performance degradation due to limb position changes, which affect signal characteristics. To address this, we introduce MyoPose, a novel and lightweight spatial synergy-based feature set for enhancing neuromechanical control. MyoPose works on effectively decoding colocated sEMG-pFMG information to improve hand gesture recognition under limb position variability while remaining computationally efficient for resource-constrained hardware. The proposed MyoPose feature combined with Linear Discriminant Analysis (LDA), achieved 87.7% accuracy in a nine-hand gesture recognition task, outperforming standard myoelectric feature sets and comparable to a state-of-the-art decision-level multimodal fusion parallel CNN. Notably, MyoPose maintained computational efficiency, achieving real-time feasibility with an estimated controller delay of 110.62 ms, well within the operational requirement of 100-125 ms, as well as ultra-light memory requirement of 0.011 KB. The novelty of this study lies in providing an effective feature set for multimodal driven hand gesture recognition, handling limb position variations with robust accuracy, and showing potential for real-time feasibility for human-machine interfaces without the need for deep learning.

Authors

  • Rami Mobarak
    Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.
  • Shen Zhang
    School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
  • Hao Zhou
    State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan, Hubei 430030, China.
  • Alessandro Mengarelli
    Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.
  • Federica Verdini
    Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131, Ancona, Italy.
  • Laura Burattini
    Information Engineering Department, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60121, Ancona, Italy.
  • Andrea Tigrini
    Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.
  • Gursel Alici
    a 1 School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong , 2522 NSW, Australia.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals