Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the...

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Bibliographic Details
Published in:Applied sciences 2021-10, Vol.11 (19), p.8940
Main Authors: Choi, Wonseok, Yang, Wonseok, Lee, Giuk, Nam, Woochul
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian optimization
feature optimization
Gait
gait phase
genetic algorithm
Genetic algorithms
Optimization
Parkinson's disease
Principal components analysis
Running
Sensors
time window
time-domain feature
Walking
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Summary:For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11198940