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Title: Identifying Muscle Imbalances in Athletes via Motion Analysis using Sensory Inputs
Authors: Vithanage, S.S.
Ratnadiwakara, M.S.
Issue Date: 26-Jul-2021
Abstract: The balance of muscle strength and length between muscles surrounding a joint should be maintained to have a healthy movement and function in human body. Muscle imbalances are mainly caused due to repetitive movement in one direction or sustained posture and the tendency of certain muscle groups to be tight or weak. In the context of collegiate athletes, the healthy practice of movements is essential since the incorrect biomechanics can result in injuries that would take a considerable amount of time to recover through rehabilitation. The current clinical methods of identifying muscle imbalances such as Movement analysis, gait and posture analysis, joint range of motion analysis and muscle length analysis are all observational evaluations that entirely depends on the domain knowledge and experience of the observer. Technical methods such as Electromyography and CT scan require expensive equipment and patients would only care to examine after having pains or dysfunctions. To address the limitations of current muscle imbalance evaluation techniques such as time, cost and domain expertise, this research propose a solution that can incorporate an athlete to self-identify the certain overractive and underractive muscle groups in their body in order to follow intervention exercises. Movement analysis; one of the commonly used methods in the context of physiotherapy to identify dysfunctions in the human musculoskeletal system, is the method used in this research to identify muscle imbalances. The Overhead Squat is used as the movement pattern to be analyzed with the aid of the motion tracking device; Orbbec Astra Pro. Two mathematical models are used to calculate the joint angles and joint distances relating to the overhead squat. The joint positional data taken from Orbbec Astra Pro is used as the input for the mathematical formula. Overhead squat movement pattern of a healthy subject is represented as graphs in order to compare graphs of a new subject to identify deviations. By identifying deviations the research propose potential overractive and underractive muscle groups that causes the muscle imbalances. The accuracy of the proposed method was more than 75%, which is a satisfactory outcome. This method will facilitate the self-detection of imbalances in the whole body at a low-cost and with less time. Further enhancements to this proposed method can bring forth benefits to the fields of sports medicine, physiotherapy as well as physical fitness.
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