Wayfinding Behavior Detection by Smartphone

Abstract

While heading to a destination, we usually rely on our cognitive map constructed by audiovisual information from maps and our sight. However, errors or gaps between the real and our cognitive map often confuse us and lead to "wayfinding". In such a wayfinding state, we tend to take actions like wandering for perceiving errors and gathering information about surrounding environment. If such behavior can be detected by smartphones, we may design new applications on the smartphones, for instance, virtual "concierge" that timely helps us when we lose our ways. Also grasping spots where people are likely to lose their ways in large museums and theme parks would be useful to install or improve the signs and directions to support visitors. In this paper, we propose a method to detect individuals' wayfinding behavior from walking features by smartphone sensors. Based on the preliminary experiment, we extract sensor data features that can be collected through Android OS without privacy concerns, and build a binary classifier of user states, "normal" and "wayfinding". Through the two field experiments with 17 and 104 subjects, we have confirmed that our classifier achieved the F-measure of 0.93 and 0.85, respectively.

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