Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition

BOUCHUT, Quentin, APPIAH, Kofi, LOTFI, Ahmad and DICKINSON, Patrick (2019). Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition. In: 2018 IEEE 20th International Conferences on High Performance Computing and Communications (HPCC). IEEE.

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Official URL: https://ieeexplore.ieee.org/document/8622923
Link to published version:: https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00185

Abstract

A recognition framework to identify six full body motion from smartphone sensory data is proposed. The proposed system relies on accelerometer, gyroscope and magnetometer data to classify user activities into six groups (sitting, standing, lying down, walking, walking up stairs and walking downstairs). The proposed solution is an improvement of a one-verse-one SVM classifier with an ensemble of different learning methods each trained to discriminate a single activity against another. The improvement presented here doesn't only focus on accuracy but also potential embedded implementation capable of performing real-time classification with mobile data from the cloud. The presented one-versus-one approach, based on a linear kernel achieved 97.50 percent accuracy on a public dataset; second best to 98.57 percent reported in literature which uses a polynomial kernel.

Item Type: Book Section
Additional Information: 20th IEEE International Conferences on High Performance Computing and Communications (HPCC), 28-30 June 2018, Exeter, UK
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00185
Related URLs:
Depositing User: Kofi Appiah
Date Deposited: 23 Aug 2018 09:24
Last Modified: 05 Feb 2019 12:15
URI: http://shura.shu.ac.uk/id/eprint/21751

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