Masud Ahmed
ABSTRACT
Sensor-based recognition of locomotion and transportation modes has numerous application domains including urban traffic monitoring, transportation planning, and healthcare. However, the use of a smartphone in a fixed position and orientation in previous research works limited the user behavior a lot. Besides, the performance of naive methods for position-independent cases was not up to the mark. In this research, we have designed a position and orientation independent deep ensemble network (POIDEN) to classify eight modes of locomotion and transportation activities. The proposed POIDEN architecture is constructed of a Recurrent Neural Network (RNN) with LSTM that is assigned the task of selecting optimum general classifiers (random forest, decision tree, gradient boosting, etc.) to classify the activity labels. We have trained the RNN architecture using an intermediate feature set (IFS), whereas, the general classifiers have been trained using a statistical classifier feature set (SCFS). The choice of a classifier by RNN is dependent upon the highest probability of those classifiers to recognize particular activity samples. We have also utilized the rotation of acceleration and magnetometer values from phone coordinate to earth coordinate, proposed jerk feature, and position insensitive features along with parameter adjustment to make the POIDEN architecture position and orientation independent. Our team "Gradient Descent" has presented this work for the "Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge".
- Anindya Das Antar, Masud Ahmed, Mohammad Shadman Ishrak, and Md Atiqur Rahman Ahad. 2018. A comparative approach to classification of locomotion and transportation modes using smartphone sensor data. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1497--1502. Google Scholar
Digital Library
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- Hristijan Gjoreski, Mathias Ciliberto, Lin Wang, Francisco Javier Ordonez Morales, Sami Mekki, Stefan Valentin, and Daniel 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
- Hristijan Gjoreski, Boštjan Kaluža, Matjaž Gams, Radoje Milić, and Mitja Luštrek. 2015. Context-based ensemble method for human energy expenditure estimation. Applied Soft Computing 37 (2015), 960--970. Google ScholarDigital Library
- Apiwat Henpraserttae, Surapa Thiemjarus, and Sanparith Marukatat. 2011. Accurate activity recognition using a mobile phone regardless of device orientation and location. In 2011 International Conference on Body Sensor Networks. IEEE, 41--46. Google ScholarDigital Library
- Ozlem Incel. 2015. Analysis of movement, orientation and rotation-based sensing for phone placement recognition. Sensors 15, 10 (2015), 25474--25506.Google ScholarCross Ref
- Vito Janko, Nina Rešçiç, Miha Mlakar, Vid Drobnič, Matjaž Gams, Gašper Slapničar, Martin Gjoreski, Jani Bizjak, Matej Marinko, and Mitja Luštrek. 2018. A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. ACM, 1511--1520. Google ScholarDigital Library
- Fen Miao, Yi He, Jinlei Liu, Ye Li, and Idowu Ayoola. 2015. Identifying typical physical activity on smartphone with varying positions and orientations. Biomedical engineering online 14, 1 (2015), 32.Google Scholar
- Joshua E Siegel, Dylan C Erb, and Sanjay E Sarma. 2017. A survey of the connected vehicle landscape---Architectures, enabling technologies, applications, and development areas. IEEE Transactions on Intelligent Transportation Systems 19, 8 (2017), 2391--2406.Google ScholarDigital Library
- Yunus Emre Ustev, Ozlem Durmaz Incel, and Cem Ersoy. 2013. User, device and orientation independent human activity recognition on mobile phones: Challenges and a proposal. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, 1427--1436. Google ScholarDigital Library
- Lin Wang, Hristijan Gjoreski, Mathias Ciliberto, Paula Lago, Kazuya Murao, Tsuyoshi Okita, and Daniel Roggen. 2019. Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019. In Proc. HASCA 2019. Google ScholarDigital Library
- Lin Wang, Hristijan Gjoreski, Mathias Ciliberto, Sami Mekki, Stefan Valentin, and Daniel 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
- Rong Yang and Baowei Wang. 2016. Pacp: A position-independent activity recognition method using smartphone sensors. Information 7, 4 (2016), 72.Google ScholarCross Ref
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