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Benchmarking the SHL Recognition Challenge with classical and deep-learning pipelines

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conference contribution
posted on 2023-06-09, 14:54 authored by Lin Wang, Hristijan GjoreskiHristijan Gjoreski, Mathias Ciliberto, Sami Mekki, Valentin Stefan, Daniel RoggenDaniel Roggen
In 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 Computers

Publisher

Association for Computing Machinery

Page range

1626-1635

Event name

HASCA Workshop at Ubicomp 2018

Event location

Singapore

Event type

workshop

Event date

8-12 October 2018

ISBN

9781450359665

Department 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 Okita

Legacy Posted Date

2018-09-05

First Open Access (FOA) Date

2018-11-15

First Compliant Deposit (FCD) Date

2018-09-04

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