A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition

• URL: http://arxiv.org/abs/2011.03042v1
• Date: Thu, 5 Nov 2020 14:31:00 GMT
• Title: A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition
• Authors: Jingjing Cao, Fukang Guo, Xin Lai, Qiang Zhou, Jinshan Dai
• Abstract summary: We propose an end-to-end Tree-Structure Convolutional neural network based framework for Multi-Resident Activity Recognition (TSC-MRAR) First, we treat each sample as an event and obtain the current event embedding through the previous sensor readings in the sliding window. Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings.
• Score: 4.619245607612873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant. While in reality, there are generally multiple residents at home, which brings greater challenge to recognize activities. In addition, many conventional approaches rely on manual time series data segmentation ignoring the inherent characteristics of events and their heuristic hand-crafted feature generation algorithms are difficult to exploit distinctive features to accurately classify different activities. To address these issues, we propose an end-to-end Tree-Structure Convolutional neural network based framework for Multi-Resident Activity Recognition (TSC-MRAR). First, we treat each sample as an event and obtain the current event embedding through the previous sensor readings in the sliding window without splitting the time series data. Then, in order to automatically generate the temporal features, a tree-structure network is designed to derive the temporal dependence of nearby readings. The extracted features are fed into the fully connected layer, which can jointly learn the residents labels and the activity labels simultaneously. Finally, experiments on CASAS datasets demonstrate the high performance in multi-resident activity recognition of our model compared to state-of-the-art techniques.

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