Development of a Real-Time Knee Extension Monitoring and Rehabilitation System: Range of Motion and Surface EMG Measurement and Evaluation

• Published in: Healthcare (Basel) Vol. 10; no. 12; p. 2544
• Main Authors: Sengchuai, Kiattisak, Kanjanaroat, Chinnakrit, Jaruenpunyasak, Jermphiphut, Limsakul, Chonnanid, Tayati, Watcharin, Booranawong, Apidet, Jindapetch, Nattha
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2022; MDPI
• Subjects: Accelerometers; Ankle; Arthritis; Camcorders; Cameras; COVID-19; Electromyography; Exercise; Hospitals; Injuries; Joints; Knee; knee extension; Methods; Osteoarthritis; Pandemics; Patient monitoring; Patient safety; Performance evaluation; Physical fitness; Physical therapists; Physical therapy; Range of motion; real-time monitoring; Rehabilitation; ROM; Sensors; Social distancing; Stroke; surface EMG; Therapeutics, Physiological; Thailand; ROM; real-time monitoring; rehabilitation; surface EMG; knee extension
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare10122544

In this paper, a real-time knee extension monitoring and rehabilitation system for people, such as patients, the elderly, athletes, etc., is developed and tested. The proposed system has three major functions. The first function is two-channel surface electromyography (EMG) signal measurement and processing for the vastus lateralis (VL) and vastus medialis (VM) muscles using a developed EMG device set. The second function is the knee extension range of motion (ROM) measurement using an angle sensor device set (i.e., accelerometer sensor). Both functions are connected and parallelly processed by the NI-myRIO embedded device. Finally, the third function is the graphical user interface (GUI) using LabVIEW, where the knee rehabilitation program can be defined and flexibly set, as recommended by physical therapists and physicians. Experimental results obtained from six healthy subjects demonstrated that the proposed system can efficiently work with real-time response. It can support multiple rehabilitation users with data collection, where EMG signals with mean absolute value (MAV) and root mean square value (RMS) results and knee extension ROM data can be automatically measured and recorded based on the defined rehabilitation program. Furthermore, the proposed system is also employed in the hospital for validation and evaluation, where bio-feedback EMG and ROM data from six patients, including (a) knee osteoarthritis, (b) herniated disc, (c) knee ligament injury, (d) ischemic stroke, (e) hemorrhagic stroke, and (f) Parkinson are obtained. Such data are also collected for one month for tracking, evaluation, and treatment. With our proposed system, results indicate that the rehabilitation people can practice themselves and know their rehabilitation progress during the time of testing. The system can also evaluate (as a primary treatment) whether the therapy training is successful or not, while experts can simultaneously review the progress and set the optimal treatment program in response to the rehabilitation users. This technology can also be integrated as a part of the Internet of Things (IoT) and smart healthcare systems.