Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties

Qi Luo; Minglei Bai; Shuhan Chen; Kai Gao; Lairong Yin; Ronghua Du

doi:10.1109/JTEHM.2023.3320715

Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties

IEEE J Transl Eng Health Med. 2023 Sep 29:12:66-75. doi: 10.1109/JTEHM.2023.3320715. eCollection 2024.

Authors

Qi Luo¹, Minglei Bai², Shuhan Chen¹, Kai Gao¹, Lairong Yin¹, Ronghua Du¹

Affiliations

¹ School of Automotive and Mechanical EngineeringChangsha University of Science and Technology Changsha 410114 China.
² School of Biomedical SciencesThe Chinese University of Hong Kong Hong Kong 999077 China.

Abstract

Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a "press-without-break" task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.

Keywords: Electromyography (EMG); biomimetic model; force control; neuromorphic computation; prosthetic control.

MeSH terms

Artificial Limbs*
Bayes Theorem
Electromyography / methods
Hand / physiology
Humans
Prosthesis Design

Grants and funding

This work was supported in part by the National Key Research and Development Program of China through the Ministry of Science and Technology of China under Grant 2017YFA0701100, in part by the National Natural Science Foundation of China under Grant 61973047, and in part by the Natural Science Foundation of Hunan Province under Grant 2023JJ40064.