Real-time detection and recommendation of thermal spots by sensing collective behaviors in paragliding

Abstract

Community intelligence is often manifested in distinct collective behavior patterns. We investigate on the exemplary use case of paragliding how real-time participatory mobile sensing can be exploited to infer collective behavior patterns and to conclude about community intelligence. In particular, we present a system to simultaneously aggregate flight information from many paraglider pilots using their location-aware mobile phones. We show that the real-time detection of collective behavior patterns that emerge among the pilots leads to an uncovering of regions with ideal thermal characteristics. Providing an intuitive visualization of this collectively gathered information can help pilots to extend their flight time and to fly longer distances. We perform a series of test flights to assess the technical feasibility as well as the real-time performance and conduct interviews with experienced pilots to evaluate user aspects and incentivization.