Evaluation of Activation Function Capability for Intent Recognition and Development of a Computerized Prosthetic Knee

Evaluation of Activation Function Capability for Intent Recognition and Development of a Computerized Prosthetic Knee

© 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...

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Bibliographic Details
Main Authors: Manutchanok Jongprasithporn, Nantakrit Yodpijit, Gary Guerra, Uttapon Khawnuan
Other Authors: King Mongkut's University of Technology North Bangkok
Format: Conference or Workshop Item
Published: 2020
Subjects:
Business, Management and Accounting
Engineering
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/50458
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Institution: Mahidol University
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Summary:© 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).

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