Framework of Sequence Chunking for Human Activity Recognition Using Wearables
Weijia Zhang, Le Qin, Wei Zhong, Xuemei Guo, Guoli Wang · 2019
Human activity recognition (HAR) is the main research area in ubiquitous computing, and most of existing approaches are based on the frameworks of sliding window segmentation and dense labeling. However, existing frameworks have some problems. For example, sliding window segmentation will cause the problem of label inconsistency, and dense labeling cannot model relationship between activities explicitly. In our paper, we propose a new framework to deal with the problems caused by these frameworks, in which HAR is treated as a sequence chunking problem and divided into the subtasks of segmentation and labeling. The purpose of the segmentation is to segment a raw sequence into different chunks that represent the corresponding activities respectively, and labeling is used to predict the corresponding label for each chunk based on segmentation results. We propose an encoder-decoder model based on convolutional neural networks to implement the proposed framework. The encoder segments a sequence to chunks based on BIO labels, and the decoder treats a chunk as a basic unit to predict the corresponding label. We conduct experiments and show that the proposed model achieves the state-of-the-art performance on both Opportunity and Hand Gesture datasets.