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Accelerometer-based transportation mode detection on smartphones

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Accelerometer-based transportation mode detection on smartphones. / Hemminki, Samuli; Nurmi, Petteri Tapio; Tarkoma, Sasu.
SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York: Association for Computing Machinery (ACM), 2013. 13.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Hemminki, S, Nurmi, PT & Tarkoma, S 2013, Accelerometer-based transportation mode detection on smartphones. in SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems., 13, Association for Computing Machinery (ACM), New York. https://doi.org/10.1145/2517351.2517367

APA

Hemminki, S., Nurmi, P. T., & Tarkoma, S. (2013). Accelerometer-based transportation mode detection on smartphones. In SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems Article 13 Association for Computing Machinery (ACM). https://doi.org/10.1145/2517351.2517367

Vancouver

Hemminki S, Nurmi PT, Tarkoma S. Accelerometer-based transportation mode detection on smartphones. In SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York: Association for Computing Machinery (ACM). 2013. 13 doi: 10.1145/2517351.2517367

Author

Hemminki, Samuli ; Nurmi, Petteri Tapio ; Tarkoma, Sasu. / Accelerometer-based transportation mode detection on smartphones. SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. New York : Association for Computing Machinery (ACM), 2013.

Bibtex

@inproceedings{960810704d0d4e69ae457558c13f10bf,
title = "Accelerometer-based transportation mode detection on smartphones",
abstract = "We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.",
author = "Samuli Hemminki and Nurmi, {Petteri Tapio} and Sasu Tarkoma",
year = "2013",
month = nov,
doi = "10.1145/2517351.2517367",
language = "English",
isbn = "9781450320276",
booktitle = "SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Accelerometer-based transportation mode detection on smartphones

AU - Hemminki, Samuli

AU - Nurmi, Petteri Tapio

AU - Tarkoma, Sasu

PY - 2013/11

Y1 - 2013/11

N2 - We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

AB - We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

U2 - 10.1145/2517351.2517367

DO - 10.1145/2517351.2517367

M3 - Conference contribution/Paper

SN - 9781450320276

BT - SenSys '13 Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems

PB - Association for Computing Machinery (ACM)

CY - New York

ER -