Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake

Author:

Adiputra Dimas12,Azizi Abdul Rahman Mohd2,Bahiuddin Irfan23,Ubaidillah 4,Imaduddin Fitrian4,Nazmi Nurhazimah2

Affiliation:

1. Electrical Engineering Department, Institut Teknologi Telkom Surabaya , Jalan Gayungan PTT , Surabaya , Indonesia

2. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia , Jalan Sultan Yahya Petra , Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur , Malaysia

3. Department of Mechanical Engineering, Vocational College, Universitas Gadjah Mada , Jalan Yacaranda Sekip Unit IV , Yogyakarta , Daerah Istimewa Yogyakarta , Indonesia

4. Mechanical Engineering Department, Faculty of Engineering, Universitas Sebelas Maret , Jalan Ir. Sutami 36 A, Kentingan , Surakarta , , Central Java , Indonesia

Abstract

Abstract A passive controlled ankle foot orthosis (PICAFO) used a passive actuator such as Magnetorheological (MR) brake to control the ankle stiffness. The PICAFO used two kinds of sensors, such as Electromyography (EMG) signal and ankle position (two inputs) to determine the amount of stiffness (one output) to be generated by the MR brake. As the overall weight and design of an orthotic device must be optimized, the sensor numbers on PICAFO wanted to be reduced. To do that, a machine learning approach was implemented to simplify the previous stiffness function. In this paper, Non-linear Autoregressive Exogeneous (NARX) neural network were used to generate the simplified function. A total of 2060 data were used to build the network with detail such as 1309 training data, 281 validation data, 281 testing data 1, and 189 testing data 2. Three training algorithms were used such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The result shows that the function can be simplified into one input (ankle position) – one output (stiffness). Optimized result was shown by the NARX neural network with 15 hidden layers and trained using Bayesian Regularization with delay 2. In this case, the testing data shows R-value of 0.992 and MSE of 19.16.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering,Environmental Engineering

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