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A Wearable Gait Analysis and Recognition Method for Parkinson’s Disease Based on Error State Kalman Filter | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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...Show More

Abstract:

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\pm3.61 cm and 0.96\pm1.24 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.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 26, Issue: 8, August 2022)
Page(s): 4165 - 4175
Date of Publication: 11 May 2022

ISSN Information:

PubMed ID: 35544509

Funding Agency:


References

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