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.

这篇论文介绍了一种基于传感器的人体活动识别方法，通过多通道时间序列分解网络（MTSDNet）进行信号分解，学习原始信号的低秩表示，提高模型的域外泛化能力，并通过层注意机制聚合分解得到的组件，来实现最终的分类结果。通过对多个公共数据集的广泛评估，证明了该方法在预测准确性和稳定性方面的优势，具有解释性和分层特性。