Ankle Fatigue Classification Using Support Vector Machines
نویسندگان
چکیده
Fall accidents are a significant problem for the elderly, in terms of both human suffering and economic losses. Localized muscle fatigue is a potential risk factor for slip-induced falls as muscle fatigue adversely affects proprioception, movement coordination and muscle reaction times leading to postural instability and gait changes. Specifically, fatigue in ankle is associated with decline in postural stability, motor performance and fall accidents in human subjects. Automated recognition of ankle fatigue condition may be advantageous in early detection of fall and injury risks. In this study, we explore the classification potential of support vector machines (SVM) in recognizing gait patterns associated with ankle fatigue utilizing an inertial measurement unit (IMU) as the wearable technology has the potential to investigate continuous kinematic changes evoked by fatigue.
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