Publication: Evaluation of Activation Function Capability for Intent Recognition and Development of a Computerized Prosthetic Knee
Issued Date
2019-01-09
Resource Type
ISSN
2157362X
21573611
21573611
Other identifier(s)
2-s2.0-85061777402
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE International Conference on Industrial Engineering and Engineering Management. Vol.2019-December, (2019), 178-182
Suggested Citation
Manutchanok Jongprasithporn, Nantakrit Yodpijit, Gary Guerra, Uttapon Khawnuan Evaluation of Activation Function Capability for Intent Recognition and Development of a Computerized Prosthetic Knee. IEEE International Conference on Industrial Engineering and Engineering Management. Vol.2019-December, (2019), 178-182. doi:10.1109/IEEM.2018.8607594 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/50458
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Title
Evaluation of Activation Function Capability for Intent Recognition and Development of a Computerized Prosthetic Knee
Abstract
© 2018 IEEE. Intent recognition is a basic requirement for computerized control of the prosthetic knee. Many scholars have used an ANN (Artificial Neural Network) and applied to a computerized prosthesis with good results. Determining an appropriate activation function in artificial neural networks is an essential issue. The main objective of this paper was to investigate the appropriate ANN activation function for intent recognition via accelerometer and gyroscope sensor data to develop a computerized prosthesis. The Feed-Forward Artificial Neural Networks (FFANN) with back-propagation learning method was used to recognize activity patterns. Efficiency of two activation functions were compared to choose an appropriate ANN activation function. Results indicate that log sigmoid function (LOGSIG) performs better than a tangent sigmoid function (TANSIG).