  Sensor-based human activity recognition is important in daily scenarios such
as smart healthcare and homes due to its non-intrusive privacy and low cost
advantages, but the problem of out-of-domain generalization caused by
differences in focusing individuals and operating environments can lead to
significant accuracy degradation on cross-person behavior recognition due to
the inconsistent distributions of training and test data. To address the above
problems, this paper proposes a new method, Multi-channel Time Series
Decomposition Network (MTSDNet). Firstly, MTSDNet decomposes the original
signal into a combination of multiple polynomials and trigonometric functions
by the trainable parameterized temporal decomposition to learn the low-rank
representation of the original signal for improving the extraterritorial
generalization ability of the model. Then, the different components obtained by
the decomposition are classified layer by layer and the layer attention is used
to aggregate components to obtain the final classification result. Extensive
evaluation on DSADS, OPPORTUNITY, PAMAP2, UCIHAR and UniMib public datasets
shows the advantages in predicting accuracy and stability of our method
compared with other competing strategies, including the state-of-the-art ones.
And the visualization is conducted to reveal MTSDNet's interpretability and
layer-by-layer characteristics.
