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

Abstract

Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.

Keywords: UR3; data acquisition; electromyography sensor; graphical user interface; rehabilitation robotics; upper limb.

Figure 1

Illustration of the overall system architecture.

Figure 2

SmartHealth, control software graphical interface in an idle state. (a) Patient tab, with the different patient registered in the software. (b) Setting tab, with a slide showing the different parameters that can be plotted.

Figure 3

Operation flowchart for the remote control system, part 1. White color defines the flowchart in the software. Blue color defines the flowchart in the robot. Yellow color defines the flowchart in the shimmer sensor.

Figure 4

Operation flowchart for the remote control system, part 2. White color defines the flowchart in the software. Blue color defines the flowchart in the robot. Yellow color defines the flowchart in the shimmer sensor.

Figure 5

Custom exercise experimentation. (a–c) Person driving the robot and the subject’s hand, with the software recording the data. (d–f) The robot driving the patient’s hand following the exact path recorded, with the software displaying the force data in $(x,y,z)$ on-the-fly.

Figure 6

General control block diagram for active-assisted rehabilitation exercise.

Figure 7

Control block diagram for active-assisted rehabilitation exercise in y and z axis for 2nd and 3rd exercise.

Figure 8

Top view of the Lateral Shoulder Rotation exercise performed by the UR3 robot. (a) Beginning of the repetition. (b) Middle of the repetition. (c) Reaching the limit of the patient’s admissible range of motion. Blue line represents the path followed by the robot [38].

Figure 9

Experimental test for passive Lateral Shoulder Rotation. (a) Robot path performed in contrast with the reference path in $(x,y)$. (b) Robot path performed in contrast with the reference path in $(x,y,z)$. (c) Robot path performed in contrast with the reference path in $(x,z)$. (d) Forces gathered by the Robotiq sensor in $(x,y,z)$ in relations with the samples.

Figure 10

Experimental test for passive Lateral Shoulder Rotation. (a) Muscle activity for the Deltoid muscle. (b) Muscle activity for the Latissimus Dorsi. (c) Robot position in x in relation to the samples.

Figure 11

Experimental test for restricted active-assisted lateral shoulder rotation. (a) Robot path performed in contrast with the reference path in $(x,y)$. (b) Robot path performed in contrast with the reference path in $(x,y,z)$. (c) Robot path performed in contrast with the reference path in $(x,z)$. (d) Forces gathered by the Robotiq sensor in $(x,y,z)$ in relations with the samples.

Figure 12

Experimental test for restricted active-assisted lateral shoulder rotation. (a) Muscle activity for the Deltoid muscle. (b) Muscle activity for the Latissimus Dorsi. (c) Robot position in x in relation to the samples.

Figure 13

Experimental test for a free Active-Assisted lateral shoulder rotation. (a) Robot path performed in contrast with the reference path in $(x,y)$. (b) Robot path performed in contrast with the reference path in $(x,y,z)$. (c) Robot path performed in contrast with the reference path in $(x,z)$. (d) Forces gathered by the Robotiq sensor in $(x,y,z)$ in relations with the samples.

Figure 14

Experimental test for a free Active-Assisted lateral shoulder rotation. (a) Muscle activity for the Deltoid muscle. (b) Muscle activity for the Latissimus Dorsi. (c) Robot position in x in relation to the samples.

Figure 15

Experimental test for an ADL based rehabilitation—drinking case.