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.
Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review / Ponciano, Vasco; Pires, Ivan Miguel; Ribeiro, Fernando Reinaldo; Marques, Gonçalo; Villasana, Maria Vanessa; Garcia, Nuno M.; Zdravevski, Eftim; Spinsante, Susanna. - In: ELECTRONICS. - ISSN 2079-9292. - ELETTRONICO. - 9:5(2020), p. 778. [10.3390/electronics9050778]
Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review
Garcia, Nuno M.;Spinsante, SusannaWriting – Review & Editing
2020-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
