Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
A Hierarchical Rule-Based Activity Recognition System with Frequency Pattern Mining
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
IEEE Transactions on Information Technology in Biomedicine
A Pattern Mining Approach to Sensor-Based Human Activity Recognition
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Information Technology in Biomedicine
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses
International Journal of Intelligent Information Technologies
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
Hi-index | 0.00 |
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.