Yugo Nakamura
Yoshinori Umetsu
ABSTRACT
In this paper, we, Ubi-NUTS Japan, introduce a multi-stage activity inference method that can recognize a user's mode of locomotion and transportation using mobile device sensors. We use the Sussex-Huawei Locomotion-Transportation (SHL) dataset to tackle the SHL recognition challenge, where the goal is to recognize 8 modes of locomotion and transportation (still, walk, run, bike, car, bus, train, and subway) activities from the inertial sensor data of a smartphone. We adopt a multi-stage approach where the 8 class classification problem is divided into multiple sub-problems considering the similarity of each activity. Multimodal sensor data collected from a mobile phone are inferred using a proposed pipeline that combines feature extraction and 4 different types of classifiers generated using the random forest algorithm. We evaluated our method using data from over 271 hours of daily activities of 1 participant and the 5-fold cross-validation. Evaluation results demonstrate that our method clearly recognizes the 8 types of activities with an average F1-score of 97%.
- Yu Zheng, Xing Xie, and Wei-Ying Ma. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2):32--39, 2010.Google Scholar
- Lin Liao, Dieter Fox, and Henry Kautz. Extracting places and activities from gps traces using hierarchical conditional random fields. The International Journal of Robotics Research, 26(1):119--134, 2007. Google ScholarDigital Library
- Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, and Wei-Ying Ma. Understanding transportation modes based on gps data for web applications. ACM Transactions on the Web (TWEB), 4(1):1, 2010. Google ScholarDigital Library
- Sussex-huawei locomotion dataset. http://www.shl-dataset.org/, 2018.Google Scholar
- H. Gjoreski, M. Ciliberto, L. Wang, F. J. O. Morales, S. Mekki, S. Valentin, and D. Roggen. The university of sussex-huawei locomotion and transportation dataset for multimodal analytics with mobile devices. IEEE Access, pages 1--1, 2018.Google ScholarCross Ref
- Timo Sztyler, Heiner Stuckenschmidt, and Wolfgang Petrich. Position-aware activity recognition with wearable devices. Pervasive and mobile computing, 38:281--295, 2017.Google Scholar
- Wanmin Wu, Sanjoy Dasgupta, Ernesto E Ramirez, Carlyn Peterson, and Gregory J Norman. Classification accuracies of physical activities using smartphone motion sensors. Journal of medical Internet research, 14(5), 2012.Google Scholar
- Samuli Hemminki, Petteri Nurmi, and Sasu Tarkoma. Accelerometer-based transportation mode detection on smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, page 13. ACM, 2013. Google ScholarDigital Library
- Ke-Yu Chen, Rahul C Shah, Jonathan Huang, and Lama Nachman. Mago: Mode of transport inference using the hall-effect magnetic sensor and accelerometer. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(2):8, 2017. Google ScholarDigital Library
- L. Wang, H. Gjoreski, K. Murao, T. Okita, and D. Roggen. Summary of the sussex-huawei locomotion-transportation recognition challenge. In Proceedings of the 6th International Workshop on Human Activity Sensing Corpus and Applications (HASCA2018), 2018. Google ScholarDigital Library
Index Terms
- Multi-Stage Activity Inference for Locomotion and Transportation Analytics of Mobile Users
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