A system called WiSDP, which is based on Wi‐Fi signals, to detect whether a Seated Dumbbell Press action is standard by using inexpensive consumer Wi‐Fi devices is proposed. Compared with the scheme based on high speed cameras and wearable sensors, Wi‐Fi devices are insensitive to light and colour, do not need wear any device, and decrease the risk of disclosing privacy. WiSDP senses environment changes through the Channel State Information which is fine‐grained physical layer information comparing to frequently used Received Signal Strength Indicator. Compared to the action recognition, action quality recognition depends on slight differences between a non‐standard action and standard actions, which makes it challenging. The authors propose an improved sliding window algorithm calculating action energy to extract Seated Dumbbell Press actions from the Channel State Information streams, estimate action quality by choosing an appropriate classifier and use Principal Component Analysis and Butterworth low‐pass filter to remove noise. The authors conduct experiments in two different scenarios and the average true positive rate of WiSDP are 94.66% and 95.11%, respectively.

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This letter proposes a cross-domain WiFi-based gesture recognition system (WiCross) based on a dynamically weighted multi-label generative adversarial network. Most existing WiFi-based gesture recognition systems are user, orientation, and environment sensitive, which limits the application of WiFi sensing. Compared with the influence of users and environments on WiFi sensing systems, the influence of orientation on WiFi sensing systems is more difficult to remove. To alleviate the confusion caused by the orientation more effectively, we arrange the transmitting and receiving antennas according to the characteristics of the Fresnel region. We propose to dynamically weight different links according to users’ orientations and use a multi-label generative adversarial network to obtain domain-independent features. More importantly, WiCross can use domain-independent features to classify some unknown gestures without modifying any code or data set. Lightweight computing resource consumption allows WiCross to respond in real-time. The experimental results show that WiCross can achieve an in-domain recognition accuracy of 93.54% and a cross-domain recognition accuracy of 93.11%

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