[1]SUN Yubo,LI Haitao,SHU Zhilin,et al.A quantitative gait assessment method based on lateral gastrocnemius sEMG for neuromodulation of Parkinson’s disease[J].CAAI Transactions on Intelligent Systems,2022,17(1):98-106.[doi:10.11992/tis.202103045]
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A quantitative gait assessment method based on lateral gastrocnemius sEMG for neuromodulation of Parkinson’s disease

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 98-106 Column: 学术论文—智能系统 Public date: 2022-01-05
Title:
A quantitative gait assessment method based on lateral gastrocnemius sEMG for neuromodulation of Parkinson’s disease
Author(s):
SUN Yubo1; 2;  LI Haitao3;  SHU Zhilin1; 2;  YU Yang4;  HAN Jianda1; 2;  LIANG Siquan3;  YU Ningbo1; 2
1. College of Artificial Intelligence, Nankai University, Tianjin 300350, China;
2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China;
3. Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China;
4. Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
Keywords:
deep brain stimulation; Parkinson’s disease; quantitative assessment; gait variability; sEMG; neural activation; muscle activation; plantar-pressure
CLC:
TP249;TP274
DOI:
10.11992/tis.202103045
Abstract:
Deep brain stimulation (DBS) is proven to be an effective neuromodulation treatment method for Parkinson’s disease (PD), and there is an urgent need for objective and quantitative assessment during neuromodulation treatment. In this work, a gait assessment method based on lateral gastrocnemius (LG) surface electromyogram (sEMG) signals is designed. The low-frequency trend in sEMG signals is removed by a smooth a priori algorithm, while important information related to PD motion abnormality is retained. The wavelet transform analysis is performed to detect heel strike and recognize gait cycle, and the sEMG data can be accordingly segmented. Then, the variability indices on neural activation and muscle activation are extracted. A gait quantitative assessment system is established, using wireless wearable equipments to synchronously acquire the sEMG and plantar pressure data. Ethical approval is granted and clinical study is conducted on PD patients treated with DBS. The proposed indices have high consistency with the traditional gait cycle time variability index, verifying effectiveness of the proposed method. What’s more, the proposed sEMG variability indices show higher discrimination capability.
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