Towards a Private Fall Injury Warning Service for Smartphone-Distracted Pedestrian

Abstract

Fast evolving smart phone technology greatly promoted consumption of service and content on the move, meanwhile raised new privacy, security and health issues, e.g., Increasing Pedestrians Multitasking with Smart phones (PMS) fall injury. Existing camera or vehicular-based safety technologies mostly aims to warn PMS based on camera observations which are limited to the coverage of camera view. To enable warning without camera, we look to study PMS injury issue by understand the internal causes of the increased injury, say the distractive multitasking phone usage activities (MPUA). In this paper, we present Safe MT: a smart phone-based PMS safety application to case study the suspicious typical MPUA that increase fall risk in daily life. Safe MT provides accurate private monitoring of phone usage activity (PUA) as well as accompanying gait style (GS). For PUA monitoring, Safe MT employees a novel out-of-band approach to infer typical PUA, e.g., Calling, messaging, without causing privacy harshness at low overhead. For efficient GS monitoring, Safe MT employed a novel gait-style classification (GSC) algorithm that overcome the challenges of subjective gait style signature, using later-binding initialization with subjective user data. We implemented the system on Android phones and validated its availability in supervised lab experiments, results show that our system can effectively identify the two parameters of MPUA. Although fall injury case are hardly recorded in 4-week real trace data, understanding of MPUA are still gained by mining the collected dataset.