On Multi-resident Activity Recognition in Ambient Smart-Homes
This work provides a benchmark for researchers and practitioners in ambient smart-home monitoring, though it is incremental as it compares existing methods without introducing new ones.
The paper tackled the lack of a comprehensive benchmark for multi-resident activity recognition in smart homes by evaluating various methods on the same datasets, finding that a recurrent neural network with gated recurrent units outperformed other models and was efficient, and that using combined activities as single labels was more effective than separate labels.
Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper we study different methods for multi-resident activity recognition and evaluate them on same sets of data. The experimental results show that recurrent neural network with gated recurrent units is better than other models and also considerably efficient, and that using combined activities as single labels is more effective than represent them as separate labels.