Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer

Human fall poses serious health risks especially among aged people. The rate of growth of elderly population to the total population is increasing every year. Besides causing injuries, fall may even lead to death if not attended immediately. This demands continuous monitoring of human movements and classifying normal low-level activities from abnormal event like fall. Most of the existing fall detection methods employ traditional classifiers such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the data and thus suffer high false positives rates. Hence, there is a need for a classifier that considers the association between patterns while classifying the input instance. This paper presents a pattern mining based classification algorithm called Frequent Bit Pattern based Associative Classification (FBPAC) that distinguishes low-level human activities from fall. The proposed system utilizes single tri-axial accelerometer for capturing motion data. Empirical studies are conducted by collecting real data from tri-axial accelerometer. Experimental results show that within a time-sensitive sliding window of 10 seconds, the proposed algorithm achieves 99% accuracy for independent activity and 92% overall accuracy for activity sequence. The algorithm gives reasonable accuracy when tested in real time.