Next Article in Journal
Reset Controller Design Based on Error Minimization for a Lane Change Maneuver
Next Article in Special Issue
Consistent Semantic Annotation of Outdoor Datasets via 2D/3D Label Transfer
Previous Article in Journal
On-Device Learning of Indoor Location for WiFi Fingerprint Approach
Previous Article in Special Issue
Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(7), 2203; https://doi.org/10.3390/s18072203

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone

1
Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK
2
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
*
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 5 July 2018 / Accepted: 6 July 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Annotation of User Data for Sensor-Based Systems)
View Full-Text   |   Download PDF [1299 KB, uploaded 9 July 2018]   |  

Abstract

Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine). View Full-Text
Keywords: human activity recognition; supervised machine learning; label noise; automatic annotation; inertial sensors; smartphone human activity recognition; supervised machine learning; label noise; automatic annotation; inertial sensors; smartphone
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

Share & Cite This Article

MDPI and ACS Style

Cruciani, F.; Cleland, I.; Nugent, C.; McCullagh, P.; Synnes, K.; Hallberg, J. Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone. Sensors 2018, 18, 2203.

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