WOODY: A Post-Process Method for Smartphone-Based Activity Recognition

In the past decade, the rapid popularization of smartphone has provided a promising direction for human activity recognition. Despite identifying a variety of movements without any complicated wearable device, the smartphone-based activity recognition is still deeply affected by the differences between users and phone locations. To overcome this problem, post-process attempts to correct the errors in the classified activity sequence. In consideration of both the activity sequence continuity and the recognition result confidence, we propose WOODY, a novel post-process method that locates and corrects the errors in a classified activity sequence just like Woody Woodpecker pecking holes to catch the pests. In our method, the recognition result is considered as the weighted observation state, and a weighted observation hidden Markov model (WOHMM) is built to model the classified activity sequence. Consequently, a sequence labeling algorithm of the WOHMM is also designed to modify those recognition results with low confidence. To validate the effectiveness of WOODY, we make a series of contrast experiments on two public data sets collected from real scenarios. The results show that WOODY is not only able to improve the recognition accuracy but also significantly enhance the robustness.