PDF(Pubmed)
Abstract：
Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of \'scalograms\', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.
摘要：
人类活动识别(HAR)与环境辅助生活（AAL）一起，是智能家居不可或缺的组成部分，体育,监视,和调查活动。为了识别日常活动，研究人员专注于轻量级，成本效益高,基于传感器的可穿戴技术与传统的基于视觉的技术一样，缺乏老年人的隐私，每个人的基本权利。然而,从一维多传感器数据中提取潜在特征是具有挑战性的。因此，这项研究的重点是通过一维多传感器数据的时频域分析从光谱图像中提取可区分的模式和深层特征。可穿戴传感器数据，特别是加速器和陀螺仪数据，作为不同日常活动的输入信号，并使用时频分析提供潜在信息。这种潜在的时间序列信息通过称为使用“scalograms”的过程映射到光谱图像中，来自连续小波变换。使用CNN等深度学习模型从活动图像中提取深度活动特征，MobileNetV3、ResNet、和GoogleNet，随后使用常规分类器进行分类。为了验证所提出的模型，使用SisFall和PAMAP2基准测试数据集。根据实验结果,使用Morlet作为具有ResNet-101和softmax分类器的母小波，该模型显示了活动识别的最佳性能，SisFall的准确率为98.4％，PAMAP2的准确率为98.1％，并且优于最先进的算法。
