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Design of sensing system for experimental modeling of soft actuator applied for finger rehabilitation

Published online by Cambridge University Press:  28 October 2021

Shokoufeh Davarzani Open the ORCID record for Shokoufeh Davarzani [Opens in a new window] ,
Mohammad Ali Ahmadi-Pajouh  and
Hamed Ghafarirad
Shokoufeh Davarzani
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran
Mohammad Ali Ahmadi-Pajouh*
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Hamed Ghafarirad
Affiliation:
Independent Mechatronics Group, Amirkabir University of Technology, Tehran, Iran Mechanical Engineering Department, Amirkabir University of Technology, Tehran, Iran
*
*Corresponding author. E-mail: pajouh@aut.ac.ir
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Abstract

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

Keywords

rehabilitationinertial sensorKalman filterexperimental modelingsoft actuator
Type
Research Article
Information
Robotica , Volume 40 , Issue 7 , July 2022 , pp. 2091 - 2111
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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