Automatic detection of different walking conditions using inertial sensor data

Abstract

Identifying different walking conditions is essential in order to monitor the activities of elderly population for active living or fast recovery of a patient following a surgery or even for prognosis and diagnosis of several conditions like Parkinson's disease. This paper looks at automatically detecting three different walking conditions (walking normally with preferred walking speed (PWS), walking while carrying a glass of water, and walking blind folded) using inertial sensor data. Tri-axial accelerometers and gyroscopes were used to acquire movement data from both feet during the three gait tasks. Five healthy young subjects undertook 10 trials per condition on a GAITRite mat. Statistical properties such as the mean, standard deviation (std), skewness (skew) and kurtosis were calculated for each trial that included several gait cycles' data. Altogether 48 features were analyzed using Fuzzy Clustering Mean (FCM) algorithm to verify the separable nature of sensor data. The results show that three clusters could be found with an almost equal number of points; however the membership was not high enough to result in complete discrete clusters. Then three different Support Vector Machine (SVM) classifiers were used to examine whether the conditions could be automatically classified based on the features that were extracted from inertial sensor data. The results indicate 83–84% of accurate classification of the three gait conditions with three SVM algorithms. The study demonstrates that the inertial sensor data could be used to classify differences in walking conditions using powerful computational intelligence techniques.