Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 7; p. 1737
• Main Authors: Li, Mengxuan, Tian, Shanshan, Sun, Linlin, Chen, Xi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.04.2019; MDPI
• Subjects: Artificial intelligence; Asymmetry; Automation; Classification; Developing countries; Disease; Dynamic stability; electrostatic field sensing; Gait; gait feature extraction; hemiparetic gait; LDCs; Measurement techniques; Methods; Pattern recognition; Questionnaires; Researchers; Sensors; Stroke; Walking; Beijing China; China; hemiparetic gait; electrostatic field sensing; walking; gait feature extraction
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19071737

Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one's gait and thereby restrict one's activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann-Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice.