Human activity recognition from triaxial accelerometer data: Feature extraction and selection methods for clustering of physical activities

Inês Machado, Ricardo Gomes, Hugo Gamboa, Vítor Paixão

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

1 Citation (Scopus)

Abstract

The demand for objectivity in clinical diagnosis has been one of the greatest challenges in Biomedical Engineering. The study, development and implementation of solutions that may serve as ground truth in physical activity recognition and in medical diagnosis of chronic motor diseases is ever more imperative. This paper describes a human activity recognition framework based on feature extraction and feature selection techniques where a set of time, statistical and frequency domain features taken from 3-dimensional accelerometer sensors are extracted. In this paper, unsupervised learning is applied to the feature representation of accelerometer data to discover the activities performed by different subjects. A feature selection framework is developed in order to improve the clustering accuracy and reduce computational costs. The features which best distinguish a particular set of activities are selected from a 180th- dimensional feature vector through machine learning algorithms. The implemented framework achieved very encouraging results in human activity recognition: an average person-dependent Adjusted Rand Index (ARI) of 99:29%±0:5% and a person-independent ARI of 88:57%±4:0% were reached.

Original languageEnglish
Title of host publicationBIOSIGNALS 2014 - 7th Int. Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
PublisherSciTePress
Pages155-162
Number of pages8
ISBN (Print)9789897580116
Publication statusPublished - 2014
Event7th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014 - Angers, Loire Valley, France
Duration: 3 Mar 20146 Mar 2014

Conference

Conference7th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
CountryFrance
CityAngers, Loire Valley
Period3/03/146/03/14

Keywords

  • Feature Extraction
  • Feature Selection
  • Physical Activity Recognition
  • Signal Processing
  • Unsupervised Learning

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