Abstract
The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results.
Highlights
Physical medicine adopts treatment protocols based on repetitive exercises to improve the functioning of the joints
time domain (TD) features are taken directly from the pre-processed EMG and classification is performed by a first fuzzy logic system (FLS) to estimate muscle contraction
The first block is for estimating muscle contraction based on four signals extracted from the EMG sensor, namely, EMG-root mean square (RMS), EMG-SST, EMG-vorder and EMG-logdetect
Summary
Physical medicine adopts treatment protocols based on repetitive exercises to improve the functioning of the joints. The proposed solution allows remote, in-home rehabilitation and incorporates a fuzzy logic-based decision support system which takes into consideration the pain felt by the patient. In addition to the possibility of complicating the situation of the affected member, one could create a state of refusal of rehabilitation by the subject who suffered enormously To cope with this problem, some studies suggest incorporating a current return, where the peak may reflect resistance exerted by the patient due to a feeling of pain [28]. EMG signal, current sensor feedback and the position encoder feedback are used to provide the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The architecture implemented based on the Message Queuing Telemetry Transport (MQTT) protocol solves the delay problem often encountered with remote control systems
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