Abnormal Gait Detection using Smartphone

Abstract

"Gait cycle is repetitive walking pattern involving steps and strides. Difference between abnormal gait and normal gait lies between gait parameters and both are compared for prediction. We are proposing a method which is cheap and using only Smartphone embedded accelerometer to extract gait parameters. The advantages are low cost and low power supply requirements with everyone having Smartphone making it user friendly. We collected data for normal and abnormal patients having various kinds of diseases. Problems such as Rheumatoid Arthritis (RA), Osteoarthritis (OA), sciatica, calcaneal spur (or heel spur), Ankylosing spondylitis, Motor Injury, polio and Rotation of knee. The classifiers used were Naives Bayes (NB), Decision Tree (DT) and Random Forest (RF) out of which RF performed best giving 91.52% accuracy on 10-fold cross validation Set. DT and NB were giving accuracy of 86.38% and 89.69%."