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. 2021 Feb 23;21(4):1537.
doi: 10.3390/s21041537.

Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

Florin Covaciu  1 , Adrian Pisla  1 , Anca-Elena Iordan  2
Affiliations

Development of a Virtual Reality Simulator for an Intelligent Robotic System Used in Ankle Rehabilitation

Florin Covaciu et al. Sensors (Basel). .

Abstract

The traditional systems used in the physiotherapy rehabilitation process are evolving towards more advanced systems that use virtual reality (VR) environments so that the patient in the rehabilitation process can perform various exercises in an interactive way, thus improving the patient's motivation and reducing the therapist's work. The paper presents a VR simulator for an intelligent robotic system of physiotherapeutic rehabilitation of the ankle of a person who has had a stroke. This simulator can interact with a real human subject by attaching a sensor that contains a gyroscope and accelerometer to identify the position and acceleration of foot movement on three axes. An electromyography (EMG) sensor is also attached to the patient's leg muscles to measure muscle activity because a patient who is in a worse condition has weaker muscle activity. The data collected from the sensors are taken by an intelligent module that uses machine learning to create new levels of exercise and control of the robotic rehabilitation structure of the virtual environment. Starting from these objectives, the virtual reality simulator created will have a low dependence on the therapist, this being the main improvement over other simulators already created for this purpose.

Keywords: ankle rehabilitation; intelligent robotic system; machine learning; sensors; simulator; virtual reality.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 2
Figure 2
(a) Device for retrieving and sending data from sensors to the Server application. (b) MyoWare—electromyography sensor
Figure 3
Figure 3
(a) Ankle movement by rotation around the Oy axis. (b) Ankle movement by rotation around Ox axis. (c) Ankle movement by rotation around Oz axis
Figure 4
Figure 4
Robotic rehabilitation structure.
Figure 6
Figure 6
User interface.
Figure 7
Figure 7
(a) Virtual patient who performs rehabilitation exercises with robotic structure. (b) Virtual human character picking apples
Figure 1
Figure 1
Interconnection of components.
Figure 5
Figure 5
Use case diagram corresponding to the server application.
Figure 8
Figure 8
Graphical representation of KNN algorithm.
Figure 9
Figure 9
Histogram for muscle activity.
Figure 10
Figure 10
Histogram for muscle activity after balancing.
Figure 11
Figure 11
k tuning.
Figure 12
Figure 12
Graphical representation of K-fold cross-validation.
Figure 13
Figure 13
Five-fold cross-validation results.
Figure 14
Figure 14
Ten-fold cross-validation results.
See this image and copyright information in PMC

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