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.

Keywords: human robot interaction; robotic rehabilitation; wrist modeling.

Figure 1

(a) Abduction/adduction wrist movements; (b) flexion/extension wrist movements.

Figure 2

3D design of the robot.

Figure 3

(a) Abduction/adduction movement; (b) Flexion/extension movement.

Figure 4

Diagram of the actual wrist and universal joint model.

Figure 5

Control architecture overview.

Figure 6

Wrist model and resulting torque.

Figure 7

Loaded robot control architecture.

Figure 8

Wrist joint control.

Figure 9

Muscle fatigue estimation.

Figure 10

Inputs fuzzy membership functions: (a) EMG-MP; (b) EMG-MF; (c) EMG-FR; (d) EMG-FSR.

Figure 11

Muscle fatigue estimation.

Figure 12

Operating system flowchart.

Figure 13

IoT platform.

Figure 14

Setup sequences: (a) measuring passive wrist components; (b) measuring wrist active components; (c) continuous exercises.

Figure 14

Setup sequences: (a) measuring passive wrist components; (b) measuring wrist active components; (c) continuous exercises.

Figure 15

Data base interface: (a) Add new subject; (b) save exercise; (c) generate report.

Figure 16

Generated reports: (a) Patient 1; (b) Patient 2; (c) Patient 3.