Comparison of machine learning regression algorithms for foot placement prediction

Foot placement early prediction is important for designing compliant controllers for wearable robotic systems. There have been many researches on human walking gait analysis, but most of them focus on historic foot placement measurement and estimation, the work on foot placement early prediction has...

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Vydané v:2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) s. 169 - 174
Hlavní autori: Chen, Xinxing, Liu, Zijian, Zhu, Jiale, Zhang, Kuangen, Leng, Yuquan, Fu, Chenglong
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 26.11.2021
Predmet:
Gaussian processes
Legged locomotion
Machine learning
Machine learning algorithms
Prediction algorithms
Support vector machines
Training data
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Shrnutí:Foot placement early prediction is important for designing compliant controllers for wearable robotic systems. There have been many researches on human walking gait analysis, but most of them focus on historic foot placement measurement and estimation, the work on foot placement early prediction has been rarely seen. This paper investigated three machine learning regression algorithms for foot placement prediction: Linear Regression, Support Vector Machine Regression and Gaussian Process Regression. The regression models were trained on the collected walking data set, and tested on the test data set, in which the subject and the walking speeds were different from those in the training data set. The results indicated that Gaussian Process Regression showed the best performance in foot placement prediction, and the prediction error decreased with the window size of the input data increasing. The experimental results demonstrated that, given the foot position information during the early 0.2 s in the swing phase, Gaussian Process Regression can predict the next foot placement. The Root Mean Squared Error was 0.0440 m and 0.0424 m along the walking direction and cross-walking direction, respectively, which was less than 5% of the average stride length. The results of this paper are expected to help researchers select a suitable regression model for gait prediction and inspire the following works.
DOI:10.1109/M2VIP49856.2021.9665043