Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking



Alves, Flávia, Gairing, Martin, Oliehoek, Frans A ORCID: 0000-0003-4372-5055 and Do, Thanh-Toan
(2020) Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking. .

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Abstract

The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors). There is a wide range of applications within the field; for instance, assisted living, security surveillance, and intelligent transportation. In HAR, the development of Activity Recognition models is dependent upon the data captured by these devices and the methods used to analyse them, which directly affect performance metrics. In this work, we address the issue of accurately recognising human activities using different Machine Learning (ML) techniques. We propose a new feature representation based on consecutive occurring observations and compare it against previously used feature representations using a wide range of classification methods. Experimental results demonstrate that techniques based on the proposed representation outperform the baselines and a better accuracy was achieved for both highly and less frequent actions. We also investigate how the addition of further features and their pre-processing techniques affect performance results leading to state-of-the-art accuracy on a Human Activity Recognition dataset.

Item Type: Conference or Workshop Item (Unspecified)
Additional Information: 28 pages, 15 figures
Uncontrolled Keywords: cs.LG, cs.LG, stat.ML
Depositing User: Symplectic Admin
Date Deposited: 26 May 2020 09:12
Last Modified: 21 Nov 2020 08:15
Related URLs:
URI: http://livrepository.liverpool.ac.uk/id/eprint/3088422