Next Article in Journal
Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography
Previous Article in Journal
Robust Depth Estimation for Light Field Microscopy
Previous Article in Special Issue
Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61–70 Years Old
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle

A Semi-Automatic Annotation Approach for Human Activity Recognition

1
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
2
Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 501; https://doi.org/10.3390/s19030501
Received: 30 November 2018 / Revised: 11 January 2019 / Accepted: 22 January 2019 / Published: 25 January 2019
  |  
PDF [1794 KB, uploaded 31 January 2019]
  |  

Abstract

Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and transportation. The development of Human Activity Recognition (HAR) algorithms involves the collection of a large amount of labelled data which should be annotated by an expert. However, the data annotation process on large datasets is expensive, time consuming and difficult to obtain. The development of a HAR approach which requires low annotation effort and still maintains adequate performance is a relevant challenge. We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and the required volume of annotated data to obtain a high performance classifier. Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. We present a comprehensive study comparing supervised and unsupervised methods with our approach on two datasets composed of daily living activities. The results showed that it is possible to reduce the required annotated data by more than 89% while still maintaining an accurate model performance. View Full-Text
Keywords: human activity recognition; machine learning; active learning; semi-supervised learning; time series; self-training human activity recognition; machine learning; active learning; semi-supervised learning; time series; self-training
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

Bota, P.; Silva, J.; Folgado, D.; Gamboa, H. A Semi-Automatic Annotation Approach for Human Activity Recognition. Sensors 2019, 19, 501.

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