To identify the real-time activities, an online algorithm need be considered. In this paper, we will first segment entire one activity as one-time interval using Bayesian online detection method instead of fixed and small length time interval. Then, we introduce two-layer random forest classification for real-time activity recognition on the smartphone by embedded accelerometers. We evaluate the performance of our method based on six activities: walking, upstairs, downstairs, sitting, standing, and laying on 30 volunteers. For the data considered, we get 92.4% overall accuracy based on six activities and 100% overall accuracy only based on dynamic activity and static activity.
Bayesian online detection; Human activity recognition; Random forest
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