Identifying activity boundaries for activity recognition in smart environments
Yi Wang, Zhong Fan, Ayomi Bandara · 2016
Activity recognition in smart environments is an important technology for assisted living and e-health. Recently there are growing interests in applying machine learning algorithms to activity recognition tasks. In this paper, we combine support vector machine (SVM) and association rule learning to improve the performance of activity recognition based on streaming sensor data in smart homes. The proposed approach allows us to accurately identify the activity boundaries, hence reducing activity recognition errors in the system.