Automatic diagnosis of lower back pain using gait patterns
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Abstract
Back pain is a common pain that mostly affects people of all ages and results in different types of disorders such as Obesity, Slipped disc, Scoliosis, and Osteoporosis, etc. The diagnosis of back pain disorder is difficult due to the extent affected by the disorder and exact biomechanical factors. This work presents a machine learning method to diagnose these disorders using the Gait monitoring system. It involves support vector machines that classify between lower back pain and normal, on the bases of 3 Gait patterns that are integrated pressure, the direction of progression, and CISP-ML. The proposed method uses 13 different features such as mean and standard deviation, etc. recorded from 62 subjects (30 normal and 32 with lower back pain). The features alone resulted in higher leave-one-out classification accuracy (LOOCV) 92%. The proposed method can be used for automatically diagnosing the lower back pain and its gait effects on the person. This model can be ported to small computing devices for self-diagnosis of lower back pain in a remote area.
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