Robust neural decoding for dexterous control of robotic hand kinematics

Jiahao Fan; Luis Vargas; Derek G Kamper; Xiaogang Hu

doi:10.1016/j.compbiomed.2023.107139

Robust neural decoding for dexterous control of robotic hand kinematics

Comput Biol Med. 2023 Aug:162:107139. doi: 10.1016/j.compbiomed.2023.107139. Epub 2023 Jun 7.

Authors

Jiahao Fan¹, Luis Vargas², Derek G Kamper², Xiaogang Hu³

Affiliations

¹ Department of Mechanical Engineering, Pennsylvania State University, University Park, USA.
² Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, USA.
³ Department of Mechanical Engineering, Pennsylvania State University, University Park, USA; Department of Kinesiology, Pennsylvania State University, University Park, USA; Department of Physical Medicine & Rehabilitation, Pennsylvania State Hershey College of Medicine, USA; Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA; Center for Neural Engineering, Pennsylvania State University, University Park, USA. Electronic address: xxh120@psu.edu.

PMID: 37301095
DOI: 10.1016/j.compbiomed.2023.107139

Abstract

Background: Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand.

Methods: High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time.

Results: Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers.

Conclusions: This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

Keywords: Hand dexterity; Hand function; Joint kinematic control; Neural decoding; Robotic hand.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biomechanical Phenomena
Electromyography / methods
Fingers / physiology
Hand / physiology
Humans
Movement / physiology
Robotic Surgical Procedures*