ubicomp18g-sub1044-cam-i7.pdf (472.74 kB)
Benchmarking the SHL Recognition Challenge with classical and deep-learning pipelines
conference contribution
posted on 2023-06-09, 14:54 authored by Lin Wang, Hristijan GjoreskiHristijan Gjoreski, Mathias Ciliberto, Sami Mekki, Valentin Stefan, Daniel RoggenDaniel RoggenIn this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. We aim to recognize eight modes of transportation (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from smartphone inertial sensors: accelerometer, gyroscope and magnetometer. The classical classifiers include naive Bayesian, decision tree, random forest, K-nearest neighbour and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We employ a post-processing scheme to improve the recognition performance. Results show that convolutional neural network operating on frequency domain raw data achieves the best performance among all the classifiers.
Funding
Activity Sensing Technologies for Mobile Users; G2015; Huawei
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersPublisher
Association for Computing MachineryExternal DOI
Page range
1626-1635Event name
HASCA Workshop at Ubicomp 2018Event location
SingaporeEvent type
workshopEvent date
8-12 October 2018ISBN
9781450359665Department affiliated with
- Engineering and Design Publications
Research groups affiliated with
- Sensor Technology Research Centre Publications
Full text available
- Yes
Peer reviewed?
- Yes
Editors
Yu Enokibori, Philipp Scholl, Pekka Siirtola, Hristijan Gjoreski, Kazuya Murao, Paula Lago, Kei Hiroi, Tsuyoshi OkitaLegacy Posted Date
2018-09-05First Open Access (FOA) Date
2018-11-15First Compliant Deposit (FCD) Date
2018-09-04Usage metrics
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