Next Article in Journal
A Novel Technique for Sterilization Using a Power Self-Regulated Single-Mode Microwave Cavity
Next Article in Special Issue
Spatial Characterization of Radio Propagation Channel in Urban Vehicle-to-Infrastructure Environments to Support WSNs Deployment
Previous Article in Journal
An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms
Previous Article in Special Issue
Amorphous SiC/c-ZnO-Based Quasi-Lamb Mode Sensor for Liquid Environments
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(6), 1287; doi:10.3390/s17061287

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, QC J8Y 3G5, Canada
This paper is an extended version of our paper published in Davila, J.; Cretu, A.-M.; Zaremba, M. Iterative Learning for Human Activity Recognition from Wearable Sensor Data. In Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016.
*
Author to whom correspondence should be addressed.
Academic Editors: Stefano Mariani, Francesco Ciucci, Dirk Lehmhus, Thomas Messervey, Alberto Vallan and Stefan Bosse
Received: 31 March 2017 / Revised: 15 May 2017 / Accepted: 24 May 2017 / Published: 7 June 2017
View Full-Text   |   Download PDF [1890 KB, uploaded 9 June 2017]   |  

Abstract

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset. View Full-Text
Keywords: large wearable sensor dataset; human locomotion; inertial measurement units; 3-axial acceleration sensors; finite impulse response; wavelet filters; iterative classifier; SVM; multi-class classification large wearable sensor dataset; human locomotion; inertial measurement units; 3-axial acceleration sensors; finite impulse response; wavelet filters; iterative classifier; SVM; multi-class classification
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Davila, J.C.; Cretu, A.-M.; Zaremba, M. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework . Sensors 2017, 17, 1287.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top