Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects

Abstract

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (> 20 blocks/minute vs. 8–18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2–4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

“…Following the electrode setup, each participant was instructed to sit and rest their forearms on a desk at an angle of approximately 45° to create a 90° elbow angle ( Figure 3 b). Subsequently, each participant was asked to conduct six hand actions, including hand open (HO), hand close (HC), wrist extension (WE), wrist flexion (WF), wrist pronation (WP), and wrist supernation (WS), as shown in Figure 3 c. These hand actions represent agonist and antagonist movements that trigger flexor and extensor muscle groups within the forearm, and thus are commonly exploited for sEMG signal processing in myoelectric control research [ 31 , 32 , 33 ].…”

“…Subsequently, each participant was asked to conduct six hand actions, including hand open (HO), hand close (HC), wrist extension (WE), wrist flexion (WF), wrist pronation (WP), and wrist supernation (WS), as shown in Figure 3c. These hand actions represent agonist and antagonist movements that trigger flexor and extensor muscle groups within the forearm, and thus are commonly exploited for sEMG signal processing in myoelectric control research [31][32][33]. Following the electrode setup, each participant was instructed to sit and rest t forearms on a desk at an angle of approximately 45° to create a 90° elbow angle (Fig 3b).…”

An Interactive Training Model for Myoelectric Regression Control Based on Human–Machine Cooperative Performance

“…Features extracted from myoelectric signals train models to estimate users' intent [28]. To learn this relationship between myoelectric activity and corresponding prosthetic movement, the training and calibration sessions usually include a virtual target-tracing task, where the participant in the experiment has to follow a visual cue.…”

Multi-Electrode EMG Spatial-Filter Implementation Based on Current Conveyors

“…Surface electromyography (sEMG) has wide applications in medical diagnosis, rehabilitation, and obtaining an alternative path of communication in EMG-based human-machine interfaces. For example, fatigue assessments can be conducted by measuring EMG signals, among other clinical applications [1][2][3][4]; hand gestures can be deduced [5]; and prostheses can be controlled by commands issued by muscle activation [6,7].…”