Next Article in Journal
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Previous Article in Journal
Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(8), 1221; doi:10.3390/s16081221

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training

1
Department of Medical Informatics, Uniklinik RWTH Aachen, Pauwelsstrasse 30, 52057 Aachen, Germany
2
Faculty of Applied Mathematics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Cracow, Poland
3
Computer Supported Learning Group, RWTH Aachen University, Ahornstrasse 55, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Athanasios V. Vasilakos
Received: 13 April 2016 / Revised: 8 July 2016 / Accepted: 26 July 2016 / Published: 3 August 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [2913 KB, uploaded 3 August 2016]   |  

Abstract

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user’s hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills. View Full-Text
Keywords: Myo armband; eLearning; gesture recognition; surface EMG; smart wearable sensors; hand hygiene; hospital-acquired infections; nosocomial infections Myo armband; eLearning; gesture recognition; surface EMG; smart wearable sensors; hand hygiene; hospital-acquired infections; nosocomial infections
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

Kutafina, E.; Laukamp, D.; Bettermann, R.; Schroeder, U.; Jonas, S.M. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training. Sensors 2016, 16, 1221.

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