Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton.
10.7507/1001-5515.202212028
- Author:
Xu ZHONG
1
;
Bi ZHANG
1
;
Jiwei LI
1
;
Liang ZHANG
2
;
Xiangnan YUAN
3
;
Peng ZHANG
4
;
Xingang ZHAO
1
Author Information
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China.
2. Department of Rehabilitation, Liaoning Provincial People's Hospital, Shenyang 110067, P. R. China.
3. Shengjing Hospital of China Medical University, Shenyang 110004, P. R. China.
4. Medical Engineering Department, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu 225003, P. R. China.
- Publication Type:Journal Article
- Keywords:
Machine learning;
Modal fusion;
Muscle synergy;
Rehabilitation assessment;
Stroke
- MeSH:
Humans;
Exoskeleton Device;
Reproducibility of Results;
Walking/physiology*;
Lower Extremity;
Algorithms;
Stroke Rehabilitation/methods*
- From:
Journal of Biomedical Engineering
2023;40(5):953-964
- CountryChina
- Language:Chinese
-
Abstract:
In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.
