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FitAssist: virtual fitness assistant based on wifi

Regular exercise offers numerous health benefits and contributes to a healthy lifestyle. Doing exercise at home is an attractive choice for many people due to its convenience and low cost. Motivated by this, we propose FitAssist in this paper, a household virtual fitness assistant capable of performing fine-grained exercise recognition and exercise quality assessment based on commercial WiFi devices. Unlike wearable devices based systems, this system is more comfortable and device-free. In addition, compared to previous Wi-Fi based exercise monitoring systems, whose performance attenuates seriously when users stand out of the First Fresnel Zone (FFZ), FitAssist does not require users to stand on or near the line of sight (LoS) path. To achieve this, FitAssist extracts features from the fine-grained WiFi channel state information (CSI) and enables both exercise recognition and user identification via deep learning techniques. Moreover, FitAssist can provide personalized workout assessment to help users obtain effective workout and prevent injury. Extensive experimental results in real settings show that FitAssist achieves average accuracies of 97% and 98% for exercise recognition and user identification respectively, as well as giving accurate and useful feedback in various scenarios, which proves its effectiveness and robustness.