Temporal-Frequency Attention-Based Human Activity Recognition Using Commercial WiFi Devices

Yang, Xiaolong; Cao, Ruoyu; Zhou, Mu; Xie, Liangbo

doi:10.1109/access.2020.3012021

Cited by 24 publications

(12 citation statements)

References 36 publications

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“…Recently, deep learning methods have been widely used in human behavior recognition. Yang et al [21] proposed a human activity recognition system with a temporal-frequency attention mechanism. In this system, a neural network model based on attention mechanism is proposed, which assigns more weight to different characteristics by imitating the human brain to focus on important information.…”

Section: Human Activity Recognition Based On Wifi-csimentioning

confidence: 99%

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

Yang

Liu

et al. 2021

International Journal of Antennas and Propagation

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With the advancement of wireless technologies and sensing methodologies, many studies have shown that wireless signals can sense human behaviors. Human activity recognition using channel state information (CSI) in commercial WiFi devices plays an important role in many applications. In this paper, a framework for human activity recognition was constructed based on WiFi CSI signal enhancement. Firstly, the sensitivity of different antennas to human activity was studied. An antenna selection algorithm was proposed, which can make a choice of the antenna automatically based on their sensitivity in accordance with different activities. Secondly, two signal enhancement approaches, which can strengthen the active signals and weaken the inactive signals, were proposed to extract the active interval caused by human activity. Finally, an activity segmentation algorithm was proposed to detect the start and end time of activity. In order to verify and evaluate the methods, extensive experiments have been conducted in real indoor environments. The experimental results have demonstrated that our solutions can eliminate a large number of redundant information brought by insensitive and inactive signals. Our research results can be put into use to improve recognition accuracy significantly and decrease the cost of recognition time.

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Section: Human Activity Recognition Based On Wifi-csimentioning

confidence: 99%

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

Yang

Liu

et al. 2021

International Journal of Antennas and Propagation

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“…Compared with the existing positioning methods, this method can achieve higher positioning accuracy through optimized APs, and can reduce the positioning overhead in the online phase. In 2020, Yang, Cao, Zhou, and Xie proposed a deviceless human activity recognition system with a timefrequency attention mechanism, which can be deployed on commercial Wi-Fi devices to recognize human daily activities [22]. In the complex environment of coal mines, the direct contact measurement method has higher reliability.…”

Section: B Related Workmentioning

confidence: 99%

Relative Position and Posture Detection of Hydraulic Support Based on Particle Swarm Optimization

Gao

Zhang

et al. 2020

IEEE Access

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The position and posture of the hydraulic support seriously affect the efficiency of coal mining and the safety of coal production. However, most of the current detection technologies have poor reliability, and the detection methods are difficult to adapt to the complex environment of coal mines. To effectively monitor the hydraulic support, and thereby improve the efficiency of coal mining, we propose a new method to detect the relative position and posture of the hydraulic support. The method is based on the mathematical idea that three points can determine a plane. Firstly, we use angle sensors and displacement sensors to build the detection device, use STM32 microprocessor to collect data, and realize real-time display of the data on the PC. Then, we conduct a single point detection experiment and a plane moving experiment, and combined the particle swarm optimization (PSO) algorithm to optimize the data. Experiment results show that the relative error of the single-point detection can reach 0.53%. Thirdly, we carry out a detection experiment of three points on the plane. Experiment results show that the detection accuracy of the two planes can reach 0.2°. Finally, to test the monitoring effect of the detection device on the hydraulic support, we carry out the relative position and posture detection experiment of the canopy. The experiment results show that the device can effectively detect the posture change of the canopy when the hydraulic support are moving. The method we use is contact measurement, which has high reliability and strong stability. The research on the relative position and posture detection of hydraulic support provides a reliable method to monitor the support working status. It lays the foundation for the intelligent control of the mining working face straightness and the perception of the support posture.

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“…The fingerprints of wireless signals or the positions of anchor points are used to detect the positions of obstacles, and it has the advantage of wide applicability (e.g., it can be used indoors or outdoors, and there can be walls or other objects in the measurement area.) [6][7][8]. Due to the characteristics of radio waves, this method only locates the positions or recognizes the behaviors of obstacles, and usually need complex equipment.…”

Section: Introductionmentioning

confidence: 99%

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

Gao

Yang

et al. 2021

IEEE Access

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Measuring the space area of obstacles is one of the important problems in obstacle localizing fields. Most of the existing research works on the localization of obstacles focus on where the obstacles are, and few of them measure both the positions and the areas of the obstacles. In this paper, we propose a Minimum convex bounding Polygon localizing algorithm based on Visible light Tracking (MPVT) in order to rapidly and accurately locate the position and area of a 2D obstacle in the environment of sparsely-deployed sensors. MPVT first determines the initial localizing light by Visible Light Tracing method (VLT). Second, it searches the first side of the Minimum Convex Bounding Polygon (MCBP) of the obstacle. Third, MPVT calculates the subsequent other sides and the vertexes of MCBP until the next side coincides with the first side. In order to evaluate the approximation degree between the actual values and the localization values in terms of areas, positions and shapes, we propose two performance evaluation indexes, i.e., the area ratio and the ratio of equivalent radius. We conducted experiments on the influence of obstacle orientation and sparseness of sensor deployment, the accuracy comparison with the existing methods, and the time complexity. Experiment results show that MPVT can accurately locate the position and area of the obstacle in the environment of sparsely-deployed sensors with low time overhead, and is suitable for low-cost obstacle localization applications. INDEX TERMS Minimum convex bounding polygon, obstacle localization, ratio of equivalent radius, sparse environment, visible light tracking

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Temporal-Frequency Attention-Based Human Activity Recognition Using Commercial WiFi Devices

Cited by 24 publications

References 36 publications

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

Relative Position and Posture Detection of Hydraulic Support Based on Particle Swarm Optimization

A Position and Area Localization Algorithm for Obstacles in the Environment of Sparsely-Deployed Sensors

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