Exploring the potential of a model-based approach to detect muscular dysfunction using surface EMG
Shoulder and lower back problems are common causes of pain and disability in our society. Some of those problems are not possible to diagnose or treat with traditional medical procedures i.e. imaging techniques, medicines, or surgeries. Those individuals who seek help from a doctor regarding chronic pain i.e. pain due to an accident, sports injuries, or unexplainable chronic pain in the musculoskeletal system more often end up with an application to find a physical therapist for future treatment. Later on in the process of weekly or biweekly appointments with a physical therapist, the physical therapist and client come up with an exercise plan that focuses on strengthening muscles that are not working as expected. The functionality of muscles varies between individuals and therefore an exercise that is supposed to strengthen a specific muscle might actually not do so for a person that has a muscle that is hyperactive and another muscle that is inactive. Instead, the hyperactive muscle could become stronger and the inactive muscle remains inactive. How is it possible to measure if the inactive muscle is for sure being activated through exercises? Surface electromyography is one of the options that can be used during the diagnostic and treatment process since the system gives feedback on muscles' performance. This study is about the use of a surface electromyography system to visualize muscle activity and the amount of myoelectricity produced by the muscles in the shoulder area and lower back to evaluate if, in a simple way, it is possible to identify subjects suffering from muscular imbalance from healthy individuals and if the surface electromyography system is reliable. Myoelectricity was recorded from muscles using a surface electromyography system from Kiso Inc. 34 subjects were a part of this research, 19 females and 15 males. Figures created from mean-standard deviation, Lissajous patterns, and the ratio between muscles along with calculating the coefficient of variation were compared between the subjects, in order to find out if there was visually a difference between healthy and unhealthy subjects. All data analysis was carried out using Python. The results showed that there was a significant difference between some of the subjects. Subjects 25 and 27 were known to have a slipped disk in the cervical or thoracic spinal column. Both of those subjects came out as abnormal from the ratio and the Lissajous pattern comparison. This could indicate that the subjects that came out as abnormal are dealing with some sort of muscular imbalance but it needs to be validated through more progressive research in collaboration with a physical therapist.
Hátækniverkfræði, Meistaraprófsritgerðir, Rafskaut, Stafræn merkjavinnsla, Áreiðanleiki (rannsóknir), Vöðvasjúkdómar, Sjúkdómsgreining, Mechatronics engineering, Electromyography, Signal processing, Reliability (Engineering), Standard deviations, Smoothing, Lissajous' curves, Ratio analysis, Musculoskeletal Diseases
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