School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510660, China.
Sensors (Basel). 2024 Jan 24;24(3):758. doi: 10.3390/s24030758.
Human activity recognition (HAR) based on wearable sensors has emerged as a low-cost key-enabling technology for applications such as human-computer interaction and healthcare. In wearable sensor-based HAR, deep learning is desired for extracting human active features. Due to the spatiotemporal dynamic of human activity, a special deep learning network for recognizing the temporal continuous activities of humans is required to improve the recognition accuracy for supporting advanced HAR applications. To this end, a residual multifeature fusion shrinkage network (RMFSN) is proposed. The RMFSN is an improved residual network which consists of a multi-branch framework, a channel attention shrinkage block (CASB), and a classifier network. The special multi-branch framework utilizes a 1D-CNN, a lightweight temporal attention mechanism, and a multi-scale feature extraction method to capture diverse activity features via multiple branches. The CASB is proposed to automatically select key features from the diverse features for each activity, and the classifier network outputs the final recognition results. Experimental results have shown that the accuracy of the proposed RMFSN for the public datasets UCI-HAR, WISDM, and OPPORTUNITY are 98.13%, 98.35%, and 93.89%, respectively. In comparison with existing advanced methods, the proposed RMFSN could achieve higher accuracy while requiring fewer model parameters.
基于可穿戴传感器的人体活动识别 (HAR) 已经成为人机交互和医疗保健等应用的低成本关键使能技术。在基于可穿戴传感器的 HAR 中，深度学习被期望用于提取人体活动特征。由于人体活动的时空动态性，需要一种特殊的深度学习网络来识别人类的时间连续活动，以提高识别准确性，从而支持高级 HAR 应用。为此，提出了一种残差多特征融合收缩网络 (RMFSN)。RMFSN 是一种改进的残差网络，由多分支框架、通道注意力收缩块 (CASB) 和分类器网络组成。特殊的多分支框架利用 1D-CNN、轻量级时间注意机制和多尺度特征提取方法，通过多个分支捕获各种活动特征。提出 CASB 自动为每个活动从各种特征中选择关键特征，分类器网络输出最终的识别结果。实验结果表明，所提出的 RMFSN 在 UCI-HAR、WISDM 和 OPPORTUNITY 公共数据集上的准确率分别为 98.13%、98.35%和 93.89%。与现有的先进方法相比，所提出的 RMFSN 可以在需要更少模型参数的情况下实现更高的准确性。
