Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment

Abstract: Individual physiotherapy is crucial in treating patients with various pain and health issues,and significantly impacts abdominal surgical outcomes and further medical problems. Recent technologicaland artificial intelligent advancements have equipped healthcare professionals with innovativetools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities tomonitor and analyze patients’ physical activity. This paper investigates the potential applicationsof mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using adataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensivecomputational methodology is introduced, incorporating traditional digital signal processing, featureextraction in both time and transform domains, and advanced classification techniques. The studyemploys a range of machine learning methods, including support vector machines, Bayesian analysis,and neural networks, to evaluate the balance of various physical activities. The proposed approachachieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motionpatterns by employing features from both the time and frequency domains using a two-layer neuralnetwork. These findings demonstrate promising applications of precise monitoring of rehabilitationexercises to increase the probability of successful surgical recovery, highlighting the potential tosignificantly enhance patient care and treatment outcomes.