Human activity recognition (HAR) based on multimodal sensors has become a
rapidly growing branch of biometric recognition and artificial intelligence.
However, how to fully mine multimodal time series data and effectively learn
accurate behavioral features has always been a hot topic in this field.
Practical applications also require a well-generalized framework that can
quickly process a variety of raw sensor data and learn better feature
representations. This paper proposes a universal multi-sensor network (UMSNet)
for human activity recognition. In particular, we propose a new lightweight
sensor residual block (called LSR block), which improves the performance by
reducing the number of activation function and normalization layers, and adding
inverted bottleneck structure and grouping convolution. Then, the Transformer
is used to extract the relationship of series features to realize the
classification and recognition of human activities. Our framework has a clear
structure and can be directly applied to various types of multi-modal Time
Series Classification (TSC) tasks after simple specialization. Extensive
experiments show that the proposed UMSNet outperforms other state-of-the-art
methods on two popular multi-sensor human activity recognition datasets (i.e.
HHAR dataset and MHEALTH dataset).

本文提出了一种适用于人类活动识别的通用多传感器网络（UMSNet），采用了新型轻量级传感器残差块（称为 LSR 块）和 Transformer 来提取序列特征的关系，经过全面实验后，证明在人类活动识别方面优于其他最先进的方法。