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WiFi sensing is an important part of the new WiFi 802.11bf standard, whichcan detect motion and measure distances. In recent years, some machine learningmethods have been proposed for human activity recognition from WiFi signals.However, to the best of our knowledge, none of these methods have exploredorientation prediction of the user using WiFi signals. Orientation predictionis particularly critical for human-machine interaction in an environment withmultiple smart devices. In this paper, we propose a data collection setup andmachine learning models for joint human orientation and activity recognitionusing WiFi signals from a single access point (AP) or multiple APs. The resultsshow feasibility of joint orientation-activity recognition in an indoorenvironment with a high accuracy.

Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction