Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data

Adnan Manzoor*, Julia S. Mollee, Aart T. van Halteren, Michel C.A. Klein

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Insufficient physical activity is a major health concern. Choosing for active transport, such as cycling and walking, can contribute to an increase in activity. Fostering a change in behavior that prefers active transport could start with automated self-monitoring of travel choices. This paper describes an experiment to validate existing algorithms for detecting significant locations, transition periods and travel modes using smartphone-based GPS data and an off-the-shelf activity tracker. A real-life pilot study was conducted to evaluate the feasibility of the approach in the daily life of young adults. A clustering algorithm is used to locate people’s important places and an analysis of the sensitivity of the different parameters used in the algorithm is provided. Our findings show that the algorithms can be used to determine whether a user travels actively or passively based on smartphone-based GPS speed data, and that a slightly higher accuracy is achieved when it is combined with activity tracker data.

Original languageEnglish
Title of host publicationComputational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings
EditorsNgoc Thanh Nguyen, Bogdan Trawinski, Gottfried Vossen, George A. Papadopoulos, Piotr Jedrzejowicz
PublisherSpringer Verlag
Pages473-483
Number of pages11
ISBN (Print)9783319670737
DOIs
Publication statusPublished - 1 Jan 2017
Event9th International Conference on Computational Collective Intelligence, ICCCI 2017 - Nicosia, Cyprus
Duration: 27 Sep 201729 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10448 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Computational Collective Intelligence, ICCCI 2017
CountryCyprus
CityNicosia
Period27/09/1729/09/17

Keywords

  • Clustering
  • Data analytics
  • Health support systems
  • Intelligent applications
  • Physical activity

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    Manzoor, A., Mollee, J. S., van Halteren, A. T., & Klein, M. C. A. (2017). Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data. In N. T. Nguyen, B. Trawinski, G. Vossen, G. A. Papadopoulos, & P. Jedrzejowicz (Eds.), Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings (pp. 473-483). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10448 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-67074-4_46