Sequential pattern-based activity recognition model for ambient computing.

In the recent years, the human activity recognition gain popularity in ambient computing. The human activity recognition is composed of identifying the daily activities of the users by observing their actions. Action identification is a more complex task than sensor data generated by each sensor. In this paper, sequential pattern-based activity recognition is proposed for identifying sequential patterns among actions on the given dataset. This support value is used as a parameter to validate the sequence. The experimental evaluation is performed on the real time dataset and it is observed that the sequential pattern approach is very beneficial in reducing the execution time and increasing the classification accuracy of the classifiers.