Abstract
Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.
Highlights
Since the knee joint bears the full weight of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis (OA) [1]
In order to enable the physician and knee OA patients to manage the rehabilitation progress, we have developed a system that can identify the type of exercise movement the user performed and detect deviations from the correct exercise movement, which can allows knee OA patients to take the full benefit of rehabilitation exercises
This system use three wearable accelerometers as signal source, and extracts the signals’ time domain feature, frequency domain feature and angle information to identify the type of exercise movement
Summary
Since the knee joint bears the full weight of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis (OA) [1]. According to the annual medical census in 2010 published by the Taiwan Department of Health, the number of patients with arthropathies and related disorders is about 14% of the total number of outpatients and inpatients in Taiwan; and 80% of patients with knee OA are above 50 years old. The number of people above the age of 50 makes up 30.6% of the total Taiwanese population in 2011, and this ratio will become higher in the future. 9.29% of the US population is diagnosed with symptomatic knee OA by the age of 60. According to the 2012 census of the U.S Department of Commerce [2], the number of U.S residents above the age of 50 made up 32.08% of the total resident population in 2010. The study of issues related to knee
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