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Journal Abstract Search
166 related items for PubMed ID: 31130834
1. A Regression-Based Framework for Quantitative Assessment of Muscle Spasticity Using Combined EMG and Inertial Data From Wearable Sensors. Zhang X, Tang X, Zhu X, Gao X, Chen X, Chen X. Front Neurosci; 2019; 13():398. PubMed ID: 31130834 [Abstract] [Full Text] [Related]
2. A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System. Yu S, Chen Y, Cai Q, Ma K, Zheng H, Xie L. Front Neurosci; 2020; 14():462. PubMed ID: 32523505 [Abstract] [Full Text] [Related]
3. Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors. Kim JY, Park G, Lee SA, Nam Y. Sensors (Basel); 2020 Mar 14; 20(6):. PubMed ID: 32183281 [Abstract] [Full Text] [Related]
7. Assessment of elbow spasticity with surface electromyography and mechanomyography based on support vector machine. Hui Wang, Lei Wang, Yun Xiang, Ning Zhao, Xiangxin Li, Shixiong Chen, Chuang Lin, Guanglin Li. Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul 14; 2017():3860-3863. PubMed ID: 29060740 [Abstract] [Full Text] [Related]
9. Spasticity assessment based on the Hilbert-Huang transform marginal spectrum entropy and the root mean square of surface electromyography signals: a preliminary study. Hu B, Zhang X, Mu J, Wu M, Wang Y. Biomed Eng Online; 2018 Feb 27; 17(1):27. PubMed ID: 29482558 [Abstract] [Full Text] [Related]
10. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors. Li Y, Zhang X, Gong Y, Cheng Y, Gao X, Chen X. Sensors (Basel); 2017 Mar 13; 17(3):. PubMed ID: 28335394 [Abstract] [Full Text] [Related]
11. Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision. Yee J, Low CY, Mohamad Hashim N, Che Zakaria NA, Johar K, Othman NA, Chieng HH, Hanapiah FA. Diagnostics (Basel); 2023 Feb 15; 13(4):. PubMed ID: 36832227 [Abstract] [Full Text] [Related]