Article: Deep activity recognition in smart buildings with commercial Wi-Fi devices Journal: International Journal of Simulation and Process Modelling (IJSPM) 2020 Vol.15 No.4 pp.369 - 378 Abstract: Activity recognition acts as a key enabler of smart building applications, such as behaviour analysis, health diagnosis and user authentication. However, existing methods either require burdensome equipment, or light and line-of-sight (LOS) working conditions. To address this challenge, we propose DeepAR, a device-free human activity recognition system with prevailing Wi-Fi signals, which circumvents the use of dedicated devices. DeepAR mainly exploits two key techniques to recognise human daily activities. Firstly, a novel principle component extraction method is presented to capture the motion-induced distortions and discard the irrelevant interferences. Secondly, deep feature maps are constructed with time and frequency domain characteristics, and a deep convolutional neural network (CNN) model is further applied to classify the activity labels. DeepAR is implemented with commercial Wi-Fi devices, and the performance is evaluated through extensive experiments. Experiment results show that DeepAR can achieve an average accuracy of 98.6% in a meeting room and 96.4% in a student office. Inderscience Publishers - linking academia, business and industry through research

Title: Deep activity recognition in smart buildings with commercial Wi-Fi devices

Authors: Qizhen Zhou; Jianchun Xing; Yuhan Zhang; Qiliang Yang

Addresses: National Defence Engineering College, Army Engineering University of PLA, Nanjing, China ' National Defence Engineering College, Army Engineering University of PLA, Nanjing, China ' National Defence Engineering College, Army Engineering University of PLA, Nanjing, China ' National Defence Engineering College, Army Engineering University of PLA, Nanjing, China

Abstract: Activity recognition acts as a key enabler of smart building applications, such as behaviour analysis, health diagnosis and user authentication. However, existing methods either require burdensome equipment, or light and line-of-sight (LOS) working conditions. To address this challenge, we propose DeepAR, a device-free human activity recognition system with prevailing Wi-Fi signals, which circumvents the use of dedicated devices. DeepAR mainly exploits two key techniques to recognise human daily activities. Firstly, a novel principle component extraction method is presented to capture the motion-induced distortions and discard the irrelevant interferences. Secondly, deep feature maps are constructed with time and frequency domain characteristics, and a deep convolutional neural network (CNN) model is further applied to classify the activity labels. DeepAR is implemented with commercial Wi-Fi devices, and the performance is evaluated through extensive experiments. Experiment results show that DeepAR can achieve an average accuracy of 98.6% in a meeting room and 96.4% in a student office.

Keywords: channel state information; CSI; wireless sensing; deep learning; principle component analysis; PCA; smart building.

DOI: 10.1504/IJSPM.2020.110175

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.4, pp.369 - 378

Received: 15 Jan 2019
Accepted: 06 May 2019
Published online: 08 Oct 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Deep activity recognition in smart buildings with commercial Wi-Fi devices

Keep up-to-date