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 | |
| 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 | Cyprus |
| City | Nicosia |
| Period | 27/09/17 → 29/09/17 |
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Keywords
- Clustering
- Data analytics
- Health support systems
- Intelligent applications
- Physical activity
Cite this
}
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.; Klein, Michel C.A.
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.
KW - Clustering
KW - Data analytics
KW - Health support systems
KW - Intelligent applications
KW - Physical activity
UR - http://www.scopus.com/inward/record.url?scp=85030866179&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030866179&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67074-4_46
DO - 10.1007/978-3-319-67074-4_46
M3 - Conference contribution
SN - 9783319670737
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 473
EP - 483
BT - Computational Collective Intelligence - 9th International Conference, ICCCI 2017, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Trawinski, Bogdan
A2 - Vossen, Gottfried
A2 - Papadopoulos, George A.
A2 - Jedrzejowicz, Piotr
PB - Springer Verlag
ER -