Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

Abstract

In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

“…The subject experienced an approximate 83% increase in joint RoM compared to the initial state (see Table 3 ), validating the robot’s efficacy as a valuable tool for improving the range of motion, exhibiting competitive performance compared to recent similar works [ 26 , 27 , 31 , 32 ]. Moreover, the evaluation of estimated joint torque showcased a 30% reduction in passive torque, an aspect scarcely reported in recent research on human joint progress post-robotic rehabilitation training [ 33 , 34 , 35 , 36 ].…”

“…The signal was amplified with a strength of 1000, 10,000 samples per second, and a sampling frequency of 100 kHz [ 26 ]. The band-pass filter used was a Butterworth type, with a frequency range of 10–2000 Hz [ 27 ]. Two channels of EMG signals were produced by placing electrodes on both sides of the relevant muscles and placing a reference electrode at the olecranon joint.…”

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

“…The same research team, led by Bouteraa, has presented in [ 33 ] a wrist rehabilitation device with a similar concept to the one given before, in addition to implementing a fuzzy classifier to estimate the patient’s muscle fatigue and wrist health status in three steps. Ref.…”

Development of a New Control System for a Rehabilitation Robot Using Electrical Impedance Tomography and Artificial Intelligence

“…According to the World Stroke Organization (WSO), stroke remains a major global health issue, ranking as the second most common cause of mortality and the third most frequent cause of both death and disability globally [ 1 ]. The impact of strokes is not only felt in terms of deaths, but also represents a significant economic burden, with an estimated global cost of more than EUR 721 billion [ 2 ].…”