SENSOR ENHANCED REHABILITATION FOR KNEE INJURIES

Abstract

Background: Knee injuries or knee pathologies are common among normal population and athletes and were frequently presented in general clinical practice. Muscle activity and the physiotherapist’s understanding of how and what muscles move during various activities and exercises is important in exercise prescription during knee rehabilitation. At present muscle monitoring during rehabilitation is largely restricted to laboratory setup and not feasible for long term monitoring under real life     conditions and difficult for the subjects interpretation for     self-supervised exercise at present existing approaches such as EMG, require bulky hardware or special attachment of leaded electrodes to the skin, making wide spread placement difficult which is limited to clinical set up. In the Physiotherapy clinical setting, it is difficult to evaluate muscle participation when a patient is performing dynamic functional activities for precise muscle group strength protocol for better and faster recovery. Aim:  to evaluate or interpret muscle activation through portable sensor based during a variety of functional tasks and custom written program that processes sensor data and generates user friendly either for the subject himself for self-supervised exercise protocol and the clinical physiotherapist too. In certain injuries, the activities of certain group of muscles will be diminished and by measuring the muscle activities, the affected muscle group will be detected. The therapist can then plan out a better strengthening protocol for the affected group of muscles. Methods: The electrodes and sensors will be placed at 6 different locations of the patient’s lower limb muscles. Then, the subject will be asked to perform 5 different dynamic activities of the lower limb, including even floor walking, vertical jumping, side-stepping, squatting, and sitting. All the activities will be performed continuously for 10 Seconds and the muscle activities will be recorded and shown the screen. The therapist or the subject During self-supervised exercise program then interprets the results for a better, effective and faster Rehabilitation program and improved patient Participation during treatment sessions. Results: The muscle sensor generates the action potential of the muscles in the form of data and graphical representation. Muscle activity graph were then plotted for five activities performed for the ten subjects tested based on the data obtained. Mean values and root mean values were also calculated. Almost 80% of the graphs obtained shows muscle activation patterns that are corresponding to the action performed when compared among the 10 subjects. Conclusion: The results obtained from the data have a high correlation to the previous studies. The design almost fully met the project’s expectation and goals set at the beginning as this device was able to measure the potential of muscles in at least six out of the ten individuals chosen during the dynamic activities. Yet, there were certain drawbacks of this device found. Thus, there are still improvements that could be made on this device for better implementation in the future for real life cases. Thus, it can be concluded that this modified wireless muscle sensor device is able to measure muscle activity and able to give visual feedback for enhanced performance and rehabilitation exercises.