WiFit: Ubiquitous bodyweight exercise monitoring with commodity Wi-Fi devices

Bodyweight exercises are effective and efficient ways to improve one's balance, flexibility, and strength without machinery or extra equipment. Prior works have been successful in monitoring aerobic exercises and free-weight exercises, but are not suitable for ubiquitous bodyweight exercise monitoring in order to provide fine-grained repetition counting information in each exercise set. In this work, we propose WiFit, a bodyweight exercises monitoring system that supports accurate repetition counting using a pair of commodity Wi-Fi devices without attaching anything to the human body. We first analyze the movement patterns of bodyweight exercises and couple them with detailed Doppler effect modeling to determine the most effective system settings. Then, by leveraging the human activity Doppler displacement stream extracted from Wi-Fi CSI signal, we have developed an impulse-based method to segment and count the number of repetitions, and analyzed specific features for classifying different types of bodyweight exercises. Extensive experiments show that WiFit achieves 99% accuracy for repetition counting and 95.8% accuracy for exercise type classification.