TY - GEN
T1 - Study on gait discrimination method by deep learning for biofeedback training optimized for individuals
AU - Osawa, Yusuke
AU - Watanuki, Keiichi
AU - Kaede, Kazunori
AU - Muramatsu, Keiichi
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - In this research, to develop a biofeedback training system where trainees can efficiently train inadequacies that do not satisfy ideal walking using a deep learning, we examine a method that discriminates between ideal walking and nonideal walking. In the experiment, to examine the walking components used for the input data, the ground reaction force and joint angle were measured when young people walked normally and when they walked with a brace, to simulate elderly motions. Further, these data were discriminated between conditions as input data using a Convolution Neural Network (CNN). The average accuracy was 79.5% when all walking components were used as input data. In addition, it is thought that it is most suitable to discriminate walking by using all walking components, in consideration of implementation in the system.
AB - In this research, to develop a biofeedback training system where trainees can efficiently train inadequacies that do not satisfy ideal walking using a deep learning, we examine a method that discriminates between ideal walking and nonideal walking. In the experiment, to examine the walking components used for the input data, the ground reaction force and joint angle were measured when young people walked normally and when they walked with a brace, to simulate elderly motions. Further, these data were discriminated between conditions as input data using a Convolution Neural Network (CNN). The average accuracy was 79.5% when all walking components were used as input data. In addition, it is thought that it is most suitable to discriminate walking by using all walking components, in consideration of implementation in the system.
KW - Biofeedback training
KW - Convolution neural network
KW - Ground reaction force
KW - Motion capture
KW - Walking assistance
UR - http://www.scopus.com/inward/record.url?scp=85059932097&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-11051-2_24
DO - 10.1007/978-3-030-11051-2_24
M3 - Conference contribution
AN - SCOPUS:85059932097
SN - 9783030110505
T3 - Advances in Intelligent Systems and Computing
SP - 155
EP - 161
BT - Intelligent Human Systems Integration 2019 - Proceedings of the 2nd International Conference on Intelligent Human Systems Integration IHSI 2019
A2 - Ahram, Tareq
A2 - Karwowski, Waldemar
PB - Springer Verlag
T2 - 2nd International Conference on Intelligent Human Systems Integration, IHSI 2019
Y2 - 7 February 2019 through 10 February 2019
ER -