A Cloud-assisted Framework for Improving Pedestrian Safety in Urban Communities using Crowdsourced Mobile Wearable & Device Data

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. In order for any pedestrian distraction detection and safety system to be useful, and to be successfully adopted by end-users, it needs to not only accurately recognize distractions, but also needs to operate in real-time and in an energy-efficient fashion. In the quest for such a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, we make the following contributions in this phase of the project: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices.