A Wearable Gait Analysis and Recognition Method for Parkinson's Disease Based on Error State Kalman Filter

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 8; pp. 4165 - 4175
• Main Authors: Liu, Ruichen, Wang, Zhelong, Qiu, Sen, Zhao, Hongyu, Wang, Cui, Shi, Xin, Lin, Fang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: abnormal gait recognition; Accelerometers; Algorithms; Attitude estimation; Bioinformatics; Biomedical measurement; Covariance matrices; Estimation; Evaluation; feature engineering; Feature recognition; Gait; Gait recognition; Gyroscopes; information fusion; Kalman filters; Kinematics; Movement disorders; Neurodegenerative diseases; Parameter modification; Parkinson's disease; phase segmentation; Segmentation; Statistics; Wearable technology
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2022.3174249

For the purpose of quantitative analysis, this paper proposes a wearable gait analysis method for Parkinson's disease (PD) to evaluates the motor ability. The error state Kalman filter (ESKF) is used for attitude estimation, and the gait parameters are modified by phase segmentation and zero velocity update (ZUPT) algorithm. In addition, this study uses gait parameters as classifier features to recognize abnormal gait, and compares the recognition effect with statistical features. The effect of our gait system is verified by comparison with the OptiTrack system, and the mean absolute error (MAE) of step length and foot clearance are 2.52<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>3.61 cm and 0.96<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>1.24 cm respectively. Forty Parkinson's patients and forty age-matched healthy people are recruited for gait comparison, the analysis results showed significant differences between the two groups. The abnormal gait recognition results show that gait features have stronger generalization ability than statistical features in leave-one-subject-out (LOSO) validation. The method proposed in this study can be applied to the gait analysis and objective evaluation of PD.