Wi-Flex

Abstract

In this paper, we are interested in startle reflex detection with WiFi signals. We propose that two parameters related to the received signal bandwidth, maximum normalized bandwidth and bandwidth-intense duration, can successfully detect reflexes and robustly differentiate them from non-reflex events, even from those that involve intense body motions (e.g., certain exercises). In order to confirm this, we need a massive RF reflex dataset which would be prohibitively laborious to collect. On the other hand, there are many available reflex/non-reflex videos online. We then propose an efficient way of translating the content of a video to the bandwidth of the corresponding received RF signal that would have been measured if there was a link near the event in the video, by drawing analogies between our problem and the classic bandwidth modeling work of J. Carson in the context of analog FM radios (Carson's Rule). This then allows us to translate online reflex/non-reflex videos to an instant large RF bandwidth dataset, and characterize optimum 2D reflex/non-reflex decision regions accordingly, to be used during real operation with WiFi. We extensively test our approach with 203 reflex events, 322 non-reflex events (including 142 intense body motion events), over four areas (including several through-wall ones), and with 15 participants, achieving a correct reflex detection rate of 90.15% and a false alarm rate of 2.49% (all events are natural). While the paper is extensively tested with startle reflexes, it is also applicable to sport-type reflexes, and is thus tested with sport-related reflexes as well. We further show reflex detection with multiple people simultaneously engaged in a series of activities. Optimality of the proposed design is also demonstrated experimentally. Finally, we conduct experiments to show the potential of our approach for providing cost-effective and quantifiable metrics in sports, by quantifying a goalkeeper's reaction. Overall, our results confirm a fast, robust, and cost-effective reflex detection system, without collecting any RF training data, or training a neural network.