Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS

Sijia Ye; Liang Tao; Shuang Gong; Yehao Ma; Jiajia Wu; Wanyi Li; Jiliang Kang; Min Tang; Guokun Zuo; Changcheng Shi

doi:10.3389/fneur.2024.1337230

Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS

Front Neurol. 2024 Apr 17:15:1337230. doi: 10.3389/fneur.2024.1337230. eCollection 2024.

Authors

Sijia Ye^{1

2

3}, Liang Tao⁴, Shuang Gong⁴, Yehao Ma⁵, Jiajia Wu^{1

3}, Wanyi Li⁴, Jiliang Kang⁴, Min Tang⁴, Guokun Zuo^{1

2

3}, Changcheng Shi^{1

2

3}

Affiliations

¹ Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
² University of Chinese Academy of Sciences, Beijing, China.
³ Ningbo Cixi Institute of Biomedical Engineering, Ningbo, China.
⁴ Department of Neurological Rehabilitation, Ningbo Rehabilitation Hospital, Ningbo, China.
⁵ Robotics Institute, Ningbo University of Technology, Ningbo, China.

Abstract

Introduction: Upper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems.

Methods: We concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (C_V) and closeness of time profile (C_T). The fNIRS signals were calculated to extract laterality index (LI).

Results: The primary findings were that C_V, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, C_V, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R² = 0.860, p < 0.001).

Discussion: This study established a linear regression model using force, C_V, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation.

Keywords: elbow flexion; fNIRS; motor assessment; sEMG; stroke.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by National Key R&D Program of China (2022YFC3601700); the Science and Technology Innovation 2025 Major Project of Ningbo (2020Z082, 2020Z022); the Key Research and Development Program of Zhejiang Province (2023C03168); the Natural Science Foundation of Zhejiang Province (LQ23C090005); the Fund of the Director of Ningbo Institute of Materials Technology and Engineering (2021SZKY0205); and the Natural Science Foundation of Ningbo (2022J042, 2023J184).