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
Correction published on 4 November 2019, see Sensors 2019, 19(21), 4792.
Open AccessArticle

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
Sensors 2016, 16(8), 1221; https://doi.org/10.3390/s16081221
Received: 13 April 2016 / Revised: 8 July 2016 / Accepted: 26 July 2016 / Published: 3 August 2016
(This article belongs to the Section Sensor Networks)
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
Show Figures

Figure 1

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. https://doi.org/10.3390/s16081221

AMA Style

Kutafina E, Laukamp D, Bettermann R, Schroeder U, Jonas SM. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training. Sensors. 2016; 16(8):1221. https://doi.org/10.3390/s16081221

Chicago/Turabian Style

Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M. 2016. "Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training" Sensors 16, no. 8: 1221. https://doi.org/10.3390/s16081221

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop