Somatosensory integration in robot-assisted motor restoration post-stroke

Legeng Lin; Wanyi Qing; Zijian Zheng; Waisang Poon; Song Guo; Shaomin Zhang; Xiaoling Hu

doi:10.3389/fnagi.2024.1491678

Somatosensory integration in robot-assisted motor restoration post-stroke

Front Aging Neurosci. 2024 Nov 6:16:1491678. doi: 10.3389/fnagi.2024.1491678. eCollection 2024.

Authors

Legeng Lin^{1

2}, Wanyi Qing^{1

2}, Zijian Zheng³, Waisang Poon⁴, Song Guo⁵, Shaomin Zhang⁶, Xiaoling Hu^{1

2}

Affiliations

¹ Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
² Research Institute for Smart Ageing (RISA), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
³ Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
⁴ Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
⁵ Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China.
⁶ Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Abstract

Disruption of somatosensorimotor integration (SMI) after stroke is a significant obstacle to achieving precise motor restoration. Integrating somatosensory input into motor relearning to reconstruct SMI is critical during stroke rehabilitation. However, current robotic approaches focus primarily on precise control of repetitive movements and rarely effectively engage and modulate somatosensory responses, which impedes motor rehabilitation that relies on SMI. This article discusses how to effectively regulate somatosensory feedback from target muscles through peripheral and central neuromodulatory stimulations based on quantitatively measured somatosensory responses in real time during robot-assisted rehabilitation after stroke. Further development of standardized recording protocols and diagnostic databases of quantitative neuroimaging features in response to post-stroke somatosensory stimulations for real-time precise detection, and optimized combinations of peripheral somatosensory stimulations with robot assistance and central nervous neuromodulation are needed to enhance the recruitment of targeted ascending neuromuscular pathways in robot-assisted training, aiming to achieve precise muscle control and integrated somatosensorimotor functions, thereby improving long-term neurorehabilitation after stroke.

Keywords: movement recovery; neuroimaging; neuromodulation; rehabilitation; robot; sensorimotor integration; somatosensory stimulation; stroke.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the University Grants Committee Research Grants Council, Hong Kong (GRF15207120, SRFS2122-5S04, GRF15304322, and GRF15304823), the Hong Kong Polytechnic University (1-ZVVP and 1-CD74), and the Innovation and Technology Fund – Guangdong-Hong Kong Technology Cooperation Funding Scheme (ITF-TCFS) (GHP/260/22SZ).