Yan Ren
ABSTRACT
The team RY presents a solution for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge, which aims at differentiating eight transportation modes with mobile phone signal sensor data in this paper. This study first extracted a set of reasonable and discriminative features after data-preprocessing. Then, decision tree bagging, random forest, lightGBM are trained separately as basic models, whose predictions are integrated and afterward smoothed. The method gets 0.65 accuracy score on validation dataset.
- Huthaifa I. Ashqar, Mohammed H. Almannaa, Mohammed Elhenawy, Hesham A. Rakha, and Leanna House. 2019. Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains. IEEE Transactions on Intelligent Transportation Systems 20, 1(2019), 244–252. https://doi.org/10.1109/TITS.2018.2817658Google Scholar
Digital Library
- Francesco Delli Priscoli, Alessandro Giuseppi, and Federico Lisi. 2020. Automatic Transportation Mode Recognition on Smartphone Data Based on Deep Neural Networks. Sensors 20, 24 (2020). https://doi.org/10.3390/s20247228Google Scholar
- Mostafa Elhoushi, Jacques Georgy, Michael Korenberg, and Aboelmagd Noureldin. 2014. Broad motion mode recognition for portable navigation. In Proceedings of the 27th International Technical Meeting, Vol. 2. 27th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2014, New York, NY, USA.Google Scholar
- Mostafa Elhoushi, Jacques Georgy, Michael Korenberg, and Aboelmagd Noureldin. 2014. Robust motion mode recognition for portable navigation independent on device usage. In Record - IEEE PLANS, Position Location and Navigation Symposium. Proceedings of IEEE/ION PLANS 2014, Monterey, CA. https://doi.org/10.1109/PLANS.2014.6851370Google Scholar
- Tao Feng and Harry J.P. Timmermans. 2013. Transportation mode recognition using GPS and accelerometer data. Transportation Research Part C: Emerging Technologies 37 (2013), 118–130. https://doi.org/10.1016/j.trc.2013.09.014Google ScholarCross Ref
- H. Gjoreski, M. Ciliberto, L. Wang, F.J.O. Morales, S. Valentin S. Mekki, and D. Roggen. 2018. The University of Sussex-Huawei locomotion and transportation dataset for multimodal analytics with mobile devices. IEEE Access 6(2018), 42592–42604.Google ScholarCross Ref
- Jeya Vikranth Jeyakumar, Eun Sun Lee, Zhengxu Xia, Sandeep Singh Sandha, Nathan Tausik, and Mani Srivastava. 2018. Deep Convolutional Bidirectional LSTM Based Transportation Mode Recognition. In Pervasive and Ubiquitous Computing and Wearable Computers (Singapore, Singapore) (UbiComp ’18). Association for Computing Machinery, New York, NY, USA, 1606–1615. https://doi.org/10.1145/3267305.3267529Google ScholarDigital Library
- L. Wang and M. Ciliberto and H. Gjoreski and P. Lago and K. Murao and T. Okita, and D. Roggen. 2021. Locomotion and transportation Mode Recognition from GPS and radio signals: Summary of SHL Challenge 2021. In Proceedings of the 2021 ACM International Joint Conference and 2021 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery, New York, NY, USA.Google Scholar
- Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, and Mani Srivastava. 2010. Using Mobile Phones to Determine Transportation Modes. ACM Trans. Sen. Netw. 6, 2, Article 13 (March 2010), 27 pages. https://doi.org/10.1145/1689239.1689243Google ScholarDigital Library
- Jian Shen, Haihang Jiang, Fei Yang, and Zhenxing Yao. 2019. Trip mode recognition using smartphone sensor data under different sampling frequencies. Web Intelligence 17, 2 (2019), 151–160. https://doi.org/10.3233/WEB-190409Google ScholarCross Ref
- L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, and D. Roggen. 2019. Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge 2019. In Proceedings of the 2019 ACM International Joint Conference and 2019 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (London, United Kingdom) (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 849–856. https://doi.org/10.1145/3341162.3344872Google ScholarDigital Library
- L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, and D. Roggen. 2020. Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge 2020. In Proceedings of the 2020 ACM International Joint Conference and 2020 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (Virtual Event, Mexico) (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 351–358. https://doi.org/10.1145/3410530.3414341Google ScholarDigital Library
- L. Wang, H. Gjoreski, M. Ciliberto, S. Mekki, S. Valentin, and D. Roggen. 2019. Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. IEEE Access 7(2019), 10870–10891.Google ScholarCross Ref
- L. Wang, H. Gjoreskia, K. Murao, T. Okita, and D. Roggen. 2018. Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (Singapore, Singapore) (UbiComp ’18). Association for Computing Machinery, New York, NY, USA, 1521–1530. https://doi.org/10.1145/3267305.3267519Google ScholarDigital Library
- Shuangquan Wang, Canfeng Chen, and Jian Ma. 2010. Accelerometer Based Transportation Mode Recognition on Mobile Phones. In 2010 Asia-Pacific Conference on Wearable Computing Systems. IEEE, Shenzhen, China, 44–46. https://doi.org/10.1109/APWCS.2010.18Google ScholarDigital Library
- Hong Zhao, Chunning Hou, Hala Alrobassy, Xiangyan Zeng, and Giovanni Pau. 2019. Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network. J. Comput. Netw. Commun. 2019 (Jan. 2019), 11 pages. https://doi.org/10.1155/2019/4967261Google Scholar
Recommendations
Data Mining for Transportation Mode Recognition from Radio-data
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable ComputersThe rapid development of urban information techniques, mobile sensors, and artificial intelligence can help to generate solutions for transportation mode recognition (TMR). The Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge ...
Road Network Enhanced Transportation Mode Recognition with an Ensemble Machine Learning Model
UbiComp/ISWC '23 Adjunct: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable ComputingParticipants of the fifth edition of SHL recognition challenge 2023 aim to recognize eight locomotion or transportation modes in a user-independent manner based on motion and GPS sensor data. The "Fighting_zsn" team proposes an ensemble machine ...
Rotation Forest: A New Classifier Ensemble Method
We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis ...
Comments