Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking

Flavia Alves, Martin Gairing, Frans Oliehoek, Thanh-Toan Do

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Place of PublicationPiscataway
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-7281-6926-2
ISBN (Print)978-1-7281-6927-9
DOIs
Publication statusPublished - 2020
EventIJCNN 2020: International Joint Conference on Neural Networks - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

ConferenceIJCNN 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Keywords

  • Machine Learning
  • Supervised learning
  • Neural networks
  • Human Activity Recognition

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