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

doi:10.1007/978-3-319-67074-4_46

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings
Editors	Ngoc Thanh Nguyen, Bogdan Trawinski, Gottfried Vossen, George A. Papadopoulos, Piotr Jedrzejowicz
Publisher	Springer Verlag
Pages	473-483
Number of pages	11
ISBN (Print)	9783319670737
DOIs	https://doi.org/10.1007/978-3-319-67074-4_46
Publication status	Published - 1 Jan 2017
Event	9th International Conference on Computational Collective Intelligence, ICCCI 2017 - Nicosia, Cyprus Duration: 27 Sep 2017 → 29 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10448 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Computational Collective Intelligence, ICCCI 2017
Country/Territory	Cyprus
City	Nicosia
Period	27/09/17 → 29/09/17

Keywords

Clustering
Data analytics
Health support systems
Intelligent applications
Physical activity

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10.1007/978-3-319-67074-4_46

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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

Manzoor, Adnan ; Mollee, Julia S. ; van Halteren, Aart T. et al. / Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data. Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings. editor / Ngoc Thanh Nguyen ; Bogdan Trawinski ; Gottfried Vossen ; George A. Papadopoulos ; Piotr Jedrzejowicz. Springer Verlag, 2017. pp. 473-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{60dd2603781b40c69395363c3ff72e27,

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

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{\textquoteright}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.",

keywords = "Clustering, Data analytics, Health support systems, Intelligent applications, Physical activity",

author = "Adnan Manzoor and Mollee, {Julia S.} and {van Halteren}, {Aart T.} and Klein, {Michel C.A.}",

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booktitle = "Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings",

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Manzoor, A, Mollee, JS, van Halteren, AT & Klein, MCA 2017, Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data. in NT Nguyen, B Trawinski, G Vossen, GA Papadopoulos & P Jedrzejowicz (eds), Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10448 LNAI, Springer Verlag, pp. 473-483, 9th International Conference on Computational Collective Intelligence, ICCCI 2017, Nicosia, Cyprus, 27/09/17. https://doi.org/10.1007/978-3-319-67074-4_46

Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data. / Manzoor, Adnan; Mollee, Julia S.; van Halteren, Aart T. et al.

Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings. ed. / Ngoc Thanh Nguyen; Bogdan Trawinski; Gottfried Vossen; George A. Papadopoulos; Piotr Jedrzejowicz. Springer Verlag, 2017. p. 473-483 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10448 LNAI).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data

AU - Manzoor, Adnan

AU - Mollee, Julia S.

AU - van Halteren, Aart T.

AU - Klein, Michel C.A.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

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KW - Data analytics

KW - Health support systems

KW - Intelligent applications

KW - Physical activity

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings

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A2 - Trawinski, Bogdan

A2 - Vossen, Gottfried

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PB - Springer Verlag

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Manzoor A, Mollee JS, van Halteren AT , Klein MCA. Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data. In Nguyen NT, Trawinski B, Vossen G, Papadopoulos GA, Jedrzejowicz P, editors, Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings. Springer Verlag. 2017. p. 473-483. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67074-4_46

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

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