  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.
