Shalaby Eman, ElShennawy Nada, Sarhan Amany
Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
Neural Comput Appl. 2022;34(8):5993-6010. doi: 10.1007/s00521-021-06787-w. Epub 2022 Jan 7.
In recent years, channel state information (CSI) in WiFi 802.11n has been increasingly used to collect data pertaining to human activity. Such raw data are then used to enhance human activity recognition. Activities such as lying down, falling, walking, running, sitting down, and standing up can now be detected with the use of information collected through CSI. Human activity recognition has a multitude of applications, such as home monitoring of patients. Four deep learning models are presented in this paper, namely: a convolution neural network (CNN) with a Gated Recurrent Unit (GRU); a CNN with a GRU and attention; a CNN with a GRU and a second CNN, and a CNN with Long Short-Term Memory (LSTM) and a second CNN. Those models were trained to perform Human Activity Recognition (HAR) using CSI amplitude data collected by a CSI tool. Experiments conducted to test the efficacy of these models showed superior results compared with other recent approaches. This enhanced performance of our models may be attributable the ability of our models to make full use of available data and to extract all data features, including high dimensionality and time sequence. The highest average recognition accuracy reached by the proposed models was achieved by the CNN-GRU, and the CNN-GRU with attention models, standing at 99.31% and 99.16%, respectively. In addition, the performance of the models was evaluated for unseen CSI data by training our models using a random split-of-dataset method (70% training and 30% testing). Our models achieved impressive results with accuracies reaching 100% for nearly all activities. For the lying down activity, accuracy obtained from the CNN-GRU model stood at 99.46%; slightly higher than the 99.05% achieved by the CNN-GRU with attention model. This confirmed the robustness of our models against environmental changes.
近年来，WiFi 802.11n中的信道状态信息（CSI）越来越多地用于收集与人类活动相关的数据。然后，这些原始数据被用于增强人类活动识别。现在，通过使用通过CSI收集的信息，可以检测到诸如躺下、摔倒、行走、跑步、坐下和站立等活动。人类活动识别有多种应用，例如对患者的家庭监测。本文提出了四种深度学习模型，即：带有门控循环单元（GRU）的卷积神经网络（CNN）；带有GRU和注意力机制的CNN；带有GRU和第二个CNN的CNN；以及带有长短期记忆（LSTM）和第二个CNN的CNN。这些模型使用CSI工具收集的CSI幅度数据进行训练，以执行人类活动识别（HAR）。为测试这些模型的有效性而进行的实验表明，与其他近期方法相比，结果更为优异。我们模型的这种增强性能可能归因于我们的模型能够充分利用可用数据并提取所有数据特征，包括高维度和时间序列。所提出的模型中，CNN-GRU以及带有注意力机制的CNN-GRU模型达到了最高的平均识别准确率，分别为99.31%和99.16%。此外，通过使用数据集随机分割方法（70%训练和30%测试）训练我们的模型，对未见过的CSI数据评估了模型的性能。我们的模型取得了令人印象深刻的结果，几乎所有活动的准确率都达到了100%。对于躺下活动，CNN-GRU模型获得的准确率为99.46%；略高于带有注意力机制的CNN-GRU模型所达到的99.05%。这证实了我们的模型对环境变化的鲁棒性。
