Identification of diseases based on the use of inertial sensors: a systematic review

Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Marques, Gonçalo Santos; Villasana, María Vanessa; Garcia, Nuno M.; Zdravevski, Eftim; Spinsante, Susanna

http://hdl.handle.net/10400.11/7082

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Authors

Ponciano, Vasco Rafael Gaspar

Pires, Ivan M.

Ribeiro, Fernando Reinaldo

Marques, Gonçalo Santos

Villasana, María Vanessa

Garcia, Nuno M.

Zdravevski, Eftim

Spinsante, Susanna

Abstract(s)

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 for the automatic recognition of different diseases, and it may powerful the different treatments with the use of less invasive and painful techniques for patients. This paper is focused in the systematic review of the studies available in the literature for the automatic recognition of different diseases with accelerometer sensors. The disease that is the most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implements for the recognition of Parkinson’s disease reported an accuracy of 94%. Other diseases are recognized in less number that will be subject of further analysis in the future.

Keywords

Wearable electronic devices Diseases Monitoring Automatic identification

URI

http://hdl.handle.net/10400.11/7082

Citation

PONCIANO, V. [et al.] (2020) - Identification of diseases based on the use of inertial sensors: a systematic review. Electronics. ISSN 2079-9292. Vol 9, nº 5, p. 1-17. Doi: 10.3390/electronics9050778