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Volume 9
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10.3390/electronics9050778
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Review

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

by
Vasco Ponciano
1,2,
Ivan Miguel Pires
3,4,*,
Fernando Reinaldo Ribeiro
1,
Gonçalo Marques
3,
Maria Vanessa Villasana
5,
Nuno M. Garcia
3,
Eftim Zdravevski
6 and
Susanna Spinsante
7
1
R&D Unit in Digital Services, Applications, and Content, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, Portugal
2
Altranportugal, 1990-096 Lisbon, Portugal
3
Institute of Telecommunications, University of Beira Interior, 6200-001 Covilha, Portugal
4
Department of Computer Science, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
5
Faculty of Health Sciences, University of Beira Interior, 6200-506 Covilha, Portugal
6
Faculty of Computer Science and Engineering, University of Cyril and Methodius, 1000 Skopje, Macedonia
7
Department of Information Engineering, Marche Polytechnic University, 60121 Ancona, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 778; https://doi.org/10.3390/electronics9050778
Submission received: 16 April 2020 / Revised: 3 May 2020 / Accepted: 6 May 2020 / Published: 8 May 2020
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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Abstract

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.
Keywords: accelerometer; wearable electronic devices; diseases; monitoring; ambulatory; automatic identification; parkinson’s disease accelerometer; wearable electronic devices; diseases; monitoring; ambulatory; automatic identification; parkinson’s disease

Share and Cite

MDPI and ACS Style

Ponciano, V.; Pires, I.M.; Ribeiro, F.R.; Marques, G.; Villasana, M.V.; Garcia, N.M.; Zdravevski, E.; Spinsante, S. Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review. Electronics 2020, 9, 778. https://doi.org/10.3390/electronics9050778

AMA Style

Ponciano V, Pires IM, Ribeiro FR, Marques G, Villasana MV, Garcia NM, Zdravevski E, Spinsante S. Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review. Electronics. 2020; 9(5):778. https://doi.org/10.3390/electronics9050778

Chicago/Turabian Style

Ponciano, Vasco, Ivan Miguel Pires, Fernando Reinaldo Ribeiro, Gonçalo Marques, Maria Vanessa Villasana, Nuno M. Garcia, Eftim Zdravevski, and Susanna Spinsante. 2020. "Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review" Electronics 9, no. 5: 778. https://doi.org/10.3390/electronics9050778

APA Style

Ponciano, V., Pires, I. M., Ribeiro, F. R., Marques, G., Villasana, M. V., Garcia, N. M., Zdravevski, E., & Spinsante, S. (2020). Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review. Electronics, 9(5), 778. https://doi.org/10.3390/electronics9050778

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