Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
College of Engineering, Beijing Forestry University, Beijing 100083, China
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Author to whom correspondence should be addressed.
Algorithms 2019, 12(12), 253; https://doi.org/10.3390/a12120253
Received: 14 October 2019
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Revised: 8 November 2019
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Accepted: 23 November 2019
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Published: 26 November 2019
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
Gait phase detection is a new biometric method which is of great significance in gait correction, disease diagnosis, and exoskeleton assisted robots. Especially for the development of bone assisted robots, gait phase recognition is an indispensable key technology. In this study, the main characteristics of the gait phases were determined to identify each gait phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is proposed for gate detection. Compared with the traditional threshold algorithm and the LSTM, the proposed algorithm has higher detection accuracy for different walking speeds and different test subjects. During the identification process, the acceleration signals obtained from the acceleration sensors were normalized to ensure that the different features had the same scale. Principal components analysis (PCA) was used to reduce the data dimensionality and the processed data were used to create the input feature vector of the LSTM-DNN algorithm. Finally, the data set was classified using the Softmax classifier in the full connection layer. Different algorithms were applied to the gait phase detection of multiple male and female subjects. The experimental results showed that the gait-phase recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8% and 92%, respectively, which is better than the other three algorithms and also verifies the effectiveness of the LSTM-DNN algorithm in practice.
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Keywords:
gait phase detection; long short-term memory network; deep neural network; acceleration signal
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MDPI and ACS Style
Zhen, T.; Yan, L.; Yuan, P. Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm. Algorithms 2019, 12, 253. https://doi.org/10.3390/a12120253
AMA Style
Zhen T, Yan L, Yuan P. Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm. Algorithms. 2019; 12(12):253. https://doi.org/10.3390/a12120253
Chicago/Turabian StyleZhen, Tao, Lei Yan, and Peng Yuan. 2019. "Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm" Algorithms 12, no. 12: 253. https://doi.org/10.3390/a12120253
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