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Human doing actions will result in WiFi distortion, which is widely exploredfor action recognition, such as the elderly fallen detection, hand signlanguage recognition, and keystroke estimation. As our best survey, past workrecognizes human action by categorizing one complete distortion series into oneaction, which we term as series-level action recognition. In this paper, weintroduce a much more fine-grained and challenging action recognition task intoWiFi sensing domain, i.e., sample-level action recognition. In this task, everyWiFi distortion sample in the whole series should be categorized into oneaction, which is a critical technique in precise action localization,continuous action segmentation, and real-time action recognition. To achieveWiFi-based sample-level action recognition, we fully analyze approaches inimage-based semantic segmentation as well as in video-based frame-level actionrecognition, then propose a simple yet efficient deep convolutional neuralnetwork, i.e., Temporal Unet. Experimental results show that Temporal Unetachieves this novel task well. Codes have been made publicly available athttps://github.com/geekfeiw/WiSLAR.

Temporal Unet: Sample Level Human Action Recognition using WiFi