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.
Highlights
In parallel with the achievements in engineering in recent years, medicine has evolved a lot, but there are still unresolved problems
This article presents a simulator using virtual reality for a robotic system that is controlled by an intelligent module that uses machine learning to optimize the ankle recovery treatment of a stroke patients, using visual stimulation in the recovery process
This intelligent module, based on the data provided by the sensors and the user’s previous results, can determine without the intervention of the therapist the level of difficulty of the exercise to be performed by the user
Summary
In parallel with the achievements in engineering in recent years, medicine has evolved a lot, but there are still unresolved problems. The patient is in real-time interaction with the system via a special sensor device that is attached to the patient’s limb On this device the sensors are connected to a microcontroller that contains a client application through which it sends data collected from the sensors via the Wi-Fi connection to a server application that is on the computer via the TCP/IP protocol, and this data is retrieved and processed by the intelligent module. Compared to other rehabilitation systems, the robotic rehabilitation system presented in this paper has as a novelty the use of an intelligent module implemented using KNN This intelligent module, based on the data provided by the sensors and the user’s previous results, can determine without the intervention of the therapist the level of difficulty of the exercise to be performed by the user
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