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A novel approach for gait analysis using activity classifier for medical analysis

Frank Vijay J, Muneeswari G

Gait analysis to recognize the activity movements using wearable sensors is a cost effective, convenient, and efficient way of providing useful information for various applications in geriatrics, assessment of the performance of athletes in term of medical application and so on. In this paper, an attempt has been made to classify simple activities such as walking, running etc. In order to capture the movements, a Bluetooth Arduino microcontroller fitted with a tri-axial acceleration sensor was employed. The Arduino was programmed to record the readings of the acceleration sensor that works in collaboration with the user’s smartphone or PC/laptop. In order to analyse the device performance and enable it to classify the activities correctly, Naive Bayes classifier was used. This device is fitted into the footwear of the user before the user performed the activity. Four protocols were considered for this analysis, namely walking, running, falling in the forward direction and falling in the backward direction. Five healthy adults were requested to perform this experiment. The results were consistent in the different environments considered and the device showed an overall accuracy of 90.67%. The average acceleration sensor reading in the walking scenario was found to be 0.287, 1.104 in the running scenario, 0.824 in the falling forward scenario and -0.779 in the falling backward scenario.