Muscular rehabilitation is essential for injury or surgery recovery by restoring
strength, flexibility, and range of motion to the affected joints and muscles.
It can also improve posture correction and performance by strengthening weak
areas, reducing the risk of injury, and managing chronic conditions like arthritis,
osteoporosis, or chronic pain. Currently, there is only physical therapy for
these problems, and the treatment is in person at a specific location, such as a
hospital or a clinic. Other works proposed mounting surface electromyography
to recognize muscle activation patterns or wrist-forearm for muscle fatigue or using
cameras to video call for sessions. Regrettably, such works put the patient’s
privacy or comfort in danger.
Our proposed solution is a radar and machine learning-based monitoring and
classification of rehabilitation exercises. This RF-based system can accurately
monitor and classify exercises that are part of the treatment for a specific need
and in the privacy of the patient’s house. The proposed solution uses the RF
reflections of the body and the environment. It uses these signals to analyze
them in a machine learning algorithm to classify the exercise the person executes.
This solution could be used anywhere in the home by any patient with minimal
setup effort. Our results, done by four subjects in their own homes, show that
the already trained model can classify with an accuracy of 87% to 97%.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/693363 |
| Date | 06 1900 |
| Creators | Sosa Gomez, Jose Maria |
| Contributors | Alouini, Mohamed-Slim, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Wang, Di, Eltawil, Ahmed |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2024-07-31, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2024-07-31. |
| Relation | http://hdl.handle.net/10754/693340, http://hdl.handle.net/10754/693359 |
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