TrailSense: A Crowdsensing System for Detecting Risky Mountain Trail Segments with Walking Pattern Analysis

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dc.contributor.authorKim, Keunseoko
dc.contributor.authorZabihi, Hengamehko
dc.contributor.authorKim, Heeyoungko
dc.contributor.authorLee, Uichinko
dc.date.accessioned2018-01-22T09:03:10Z-
dc.date.available2018-01-22T09:03:10Z-
dc.date.created2017-12-26-
dc.date.created2017-12-26-
dc.date.issued2017-09-
dc.identifier.citationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, v.1, no.3-
dc.identifier.issn2474-9567-
dc.identifier.urihttp://hdl.handle.net/10203/237717-
dc.description.abstractTrail surface information is critical in preventing 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. First, we collected data from 14 climbers for a mountain trail which includes 13 risky segments. The average accuracy of individuals is approximately 80%, but after clustering the results, our system can accurately identify all the risky segments. We then collected an additional dataset from five climbers in two different mountain trails, which have 10 risky segments in total. Our results show that the model trained in one trail can be used to accurately identify all the risky segments in the other trail, which documents the generalizability of our system.-
dc.languageEnglish-
dc.publisherACM-
dc.titleTrailSense: A Crowdsensing System for Detecting Risky Mountain Trail Segments with Walking Pattern Analysis-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume1-
dc.citation.issue3-
dc.citation.publicationnameProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies-
dc.identifier.doi10.1145/3131893-
dc.contributor.localauthorKim, Heeyoung-
dc.contributor.localauthorLee, Uichin-
dc.description.isOpenAccessN-
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