research-article

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

Authors:

Hengameh Zabihi,

Uichin LeeAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 65, Pages 1 - 31

https://doi.org/10.1145/3131893

Published: 11 September 2017 Publication History

Abstract

Trail 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.

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Cited By

Orzikulova AXiao HLi ZYan YWang YShi YGhassemi MLee SDey AXu X(2024)Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642747(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642747
Kim ILee U(2024)Navigating User-System Gaps: Understanding User-Interactions in User-Centric Context-Aware Systems for Digital Well-being InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641979(1-15)Online publication date: 11-May-2024
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Yang GXie GWang JHe XGao LLiu Y(2024)Adaptive task recommendation based on reinforcement learning in mobile crowd sensingApplied Intelligence10.1007/s10489-023-05247-354:2(2277-2299)Online publication date: 1-Jan-2024
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Index Terms

TrailSense: A Crowdsensing System for Detecting Risky Mountain Trail Segments with Walking Pattern Analysis
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile devices

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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https://dl.acm.org/doi/10.1145/3613904.3642747
Kim ILee U(2024)Navigating User-System Gaps: Understanding User-Interactions in User-Centric Context-Aware Systems for Digital Well-being InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641979(1-15)Online publication date: 11-May-2024
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Yang GXie GWang JHe XGao LLiu Y(2024)Adaptive task recommendation based on reinforcement learning in mobile crowd sensingApplied Intelligence10.1007/s10489-023-05247-354:2(2277-2299)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05247-3
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