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).
翻译:以多式联运传感器为基础的人类活动识别(HAR)已成为生物鉴别识别和人工智能的一个迅速增长的分支,然而,如何充分挖掘多式联运时间序列数据并有效学习准确的行为特征一直是该领域的一个热门话题;实际应用还需要一个广泛化的框架,能够迅速处理各种原始传感器数据,并学习更好的特征表现;本文件提议为人类活动识别建立一个通用的多传感器网络(UMSNet),特别是,我们提议一个新的轻质传感器残余块(称为LSR块),通过减少激活功能和正常化层的数量,增加倒置的瓶盖结构和组合组合来改进性能;然后,变换器被用来提取一系列特征之间的关系,以实现人类活动的分类和承认;我们的框架有一个明确的结构,可以在简单专业化之后直接应用于各类多时序系列分类任务;广泛的实验显示,拟议的UMSNet在两种流行的多传感器识别数据集(i.HHARSet和MHHAALTH)方面比其他最先进的方法要好。