Sensor data for Human Activity Recognition: feature representation and Benchmarking

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


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 Conference Proceedings2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
EditorsAsim Roy
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
Publication statusPublished - 2020
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020 (Proceedings) (Website)

Publication series

NameProceedings of the International Joint Conference on Neural Networks
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407


ConferenceIEEE International Joint Conference on Neural Networks 2020
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow
Internet address


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

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