Abstract
The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.
Highlights
Human activity recognition is an important research problem in the social life, pervasive computing, and security monitoring fields [1,2,3]
We explore the corresponding relationship between skeleton joints and channel state information (CSI) to analyze the characteristics of an activity
It shows that the accuracy using SVM outperforms other classification algorithms and 10 subcarriers obtained by subcarrier selection mechanism increase 4.26% when compared with activity recognition using 30 subcarriers. Three antennae such as A, B, and C increase the diversity of CSI data and keep more than 80% of activity recognition accuracy
Summary
Human activity recognition is an important research problem in the social life, pervasive computing, and security monitoring fields [1,2,3]. Researchers leverage the collecting information via sensors to recognize human behavior and analyze human health condition. It has several limitations such as increasing the burden of users, the inconvenience of routine life, and sensors with limited power. Vision-based activity recognition has been popular and achieves high accuracy. Kinect-based activity recognition solves the lightenvironment problem and can track the skeleton joints of an activity with high accuracy, it cannot recognize the imperfect activity due to the crowded room, the presence of obstacles, and out of the monitoring range
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