TrailSense

Abstract

Trail surface information is critical to prevent from the mountain accidents such as falls and slips. In this paper, we propose a new mobile crowdsensing system that automatically infers whether trail segments are risky to climb, by using sensor data collected from multiple hikers' smartphones. We extract cyclic gait based features from walking motion data to train machine learning models, and multiple hikers' results are then aggregated for robust classification. We evaluate our system with two real-world datasets. We firstly collect 14 climbers' data in a mountain trail which includes 13 risky segments. The average accuracy of individuals is about 80%, but after clustering the results, our system can accurately identify all the risky segments. We then collect an additional dataset from five climbers in two different mountain trails, which have 10 risky segments in total. Our results shows that the model trained in one trail can be used to accurately identify all the risky segments in the other trail, which documents generalizability of our system.