A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

Abstract

Background: At present, the gesture recognition using sEMG signals requires vast amounts of training data or limits to a few hand movements. This paper presents a novel dynamic energy model that can decode continuous hand actions with force information, by training small amounts of sEMG data. Method: As activating the forearm muscles, the corresponding fingers are moving or tend to move (namely exerting force). The moving fingers store kinetic energy, and the fingers with moving trends store potential energy. The kinetic and potential energy of fingers is dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. At this certain moment, the sum of the two energies is constant. We regarded energy mode with the same direction of acceleration of each finger, but likely different movements, as the same one, and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy mode, to determine the hand action, including speed and force adaptively. This theory imitates the self-adapting mechanism in the actual task; thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) decoding untrained configurations, (2) decoding the amount of single-finger energy, and (3) real-time control. Results:(1) Participants completed the untrained hand movements (100 /100, p < 0.0001). (2) The test of pricking balloon with a needle tip was designed with significantly better than chance (779 /1000, p < 0.0001).(3) The test of punching a hole in the plasticine on the balloon was with over 95% success rate (97.67±5.04 %, p <0.01). Conclusion:The model can achieve continuous hand actions with force information, by training small amounts of sEMG data, which reduces trained complexity.

Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface

“…To assess each subject’s ability to perform the movements without any assistance, each movement was scored by a licensed occupational therapist based on a scoring scheme adapted from the Action Research Arm Test (ARAT) [ 18 ]. The “observed movement score” was ranked using the following categories: 0 = no movement; 1 = incomplete range of motion; 2 = complete range of motion but impaired; 3 = normal.…”

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

“…Moreover, contrary to most methods based on steady-state sEMG, study [155] employed LR with elastic net regularization for accurate real-time prediction of grip force using a single transient sEMG activation, showing promising results even for amputees. Lastly, study [156] applied the energy conservation and transfer theory, stating that kinetic and potential energy within each finger dynamically interconvert and distribute within a given muscle activation level, but the total energy across all fingers remains constant. It initially extracted MS features with ICA, then deduced each finger energy under the extreme conditions of complete fixation and free movement, finally employing ANN to learn the real-time mapping between MS features and finger energy.…”

Active upper limb prostheses: a review on current state and upcoming breakthroughs

“…Muscle synergies capture muscle activation invariance during motor production and can be exploited as control variables for ULP, with aim of obtaining a biomimetic human-like behavior (d'Avella and Bizzi 2005). The main idea is to extract motion primitives from muscle synergies and combine them to generate complex arm movements (Jiang et al 2013, Liu et al 2021a. Furui et al (2019) propose a biomimetic control based on muscle synergies to extract motion primitives and combine them to generate complex movements.…”