The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
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.
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Cites
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Cited by
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., & Jensen, M. M. (2015) · Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/2809695.2809718 · Cited by 714
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
A Survey of Online Activity Recognition Using Mobile Phones
Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2015) · Sensors · doi:10.3390/s150102059 · Cited by 489
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches
Vaizman, Y., Ellis, K., & Lanckriet, G. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.3971131 · Cited by 324
Multimodal Deep Learning for Activity and Context Recognition
Radu, V., Tong, C., Bhattacharya, S., Lane, N. D., Mascolo, C., Marina, M. K., & Kawsar, F. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3161174 · Cited by 281
Metadata
- Authors
- Hemminki, S., Nurmi, P., & Tarkoma, S.
- Venue
- Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
- Publisher
- ACM
- Published
- 2013-11-11
- Pages
- 1-14
- OpenAlex
- W2108467170
- Cited by
- 435
- References
- 34
- FWCI
- 26.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hemminki, S., Nurmi, P., & Tarkoma, S. (2013). Accelerometer-based transportation mode detection on smartphones. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. https://doi.org/10.1145/2517351.2517367
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