SHL Challenge
[1] L. Wang, H. Gjoreski, M Ciliberto, P. Lago, K. Murao, T. Okita, D. Roggen, “Three-Year review of the 2018–2020 SHL challenge on transportation and locomotion mode recognition from mobile sensors,” Frontiers in Computer Science, 3(713719): 1-24, Sep. 2021. [pdf] (Outstanding Article Award)
[2] L. Wang, M. Ciliberto, H. Gjoreski, P. Lago, K. Murao, T. Okita, and D. Roggen, “Locomotion and transportation mode recognition from GPS and radio signals: Summary of SHL challenge 2021,” Adjunct Proc. 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proc. 2021 ACM International Symposium on Wearable ComputersSeptember(UbiComp’ 21), 412-422, Virtual Event, 2021. [pdf]
[3] L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, and D. Roggen, “Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2020,” Proc. 2020 ACM International Joint Conference and 2020 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp’ 20), 351-358, Virtual Event, Mexico, 2020. [pdf]
[4] L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, and D. Roggen, “Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019,” in Proc. 2019 ACM International Joint Conference and 2019 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 849-856, 2019. [pdf]
[5] L. Wang, H. Gjoreski, K. Murao, T. Okita, and D. Roggen, “Summary of the Sussex-Huawei locomotion-transportation recognition challenge,” in Proc. 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 1521-1530, 2018. [pdf]
[6] L. Wang, H. Gjoreski, M. Ciliberto, S. Mekki, S. Valentin, and D. Roggen, “Benchmarking the SHL recognition challenge with classical and deep-learning pipelines,” in Proc. 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 1626-1635, 2018. [pdf]
SHL Dataset
[1] L. Wang, H. Gjoreski, M. Ciliberto, S. Mekki, S. Valentin, and D. Roggen, “Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset,” IEEE Access 7 (2019): 10870-10891. [pdf]
[2] H. Gjoreski, M. Ciliberto, L. Wang, F. J. O. Morales, S. Mekki, S. Valentin, D. Roggen. “The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices.” IEEE Access 6 (2018): 42592-42604. [pdf]
[3] H. Gjoreski, M. Ciliberto, F. J. Ordoñez Morales, D. Roggen, S. Mekki, S. Valentin. “A versatile annotated dataset for multimodal locomotion analytics with mobile devices.” in Proc. ACM Conference on Embedded Networked Sensor Systems, 2017. [pdf]
[4] M. Ciliberto, F. J. Ordoñez Morales, H. Gjoreski, D. Roggen, S.Mekki, S.Valentin. “High reliability Android application for multidevice multimodal mobile data acquisition and annotation.” in Proc. ACM Conference on Embedded Networked Sensor Systems, 2017. [pdf]