Given the extreme diffusion of Alzheimer's disease (AD) and Parkinson's disease (PD), the necessity for a solution to early detect neurological symptoms of such diseases strongly arose. According to the medical literature, such early detection can be achieved by exploiting the correlation between PD and AD and some external symptoms: the Essential Tremor (ET) and the number of Eye Blinks (EBs). In this paper we present NeuroGlasses, a prototype of sensored glasses able to detect the presence of ET of the head and to count the number of EBs at the same time. To the best of authors' knowledge this is the first attempt to monitor such external symptoms with a transparent and wearable device without any a-priori training. Numerical results prove the reliability of the proposed approach: NeuroGlasses are able to i) correctly recognize the ET with an overall accuracy above 97% and ii) count the number of EBs with a Root Mean Square Error (RMSE) around 0.4.

NeuroGlasses: A Wearable Prototype for Early Detection of Neurological Symptoms

Sciarrone A.;Bisio I.;Garibotto C.;Lavagetto F.;Hamedani M.;Prada V.;Stara S.;Schenone A.;
2020

Abstract

Given the extreme diffusion of Alzheimer's disease (AD) and Parkinson's disease (PD), the necessity for a solution to early detect neurological symptoms of such diseases strongly arose. According to the medical literature, such early detection can be achieved by exploiting the correlation between PD and AD and some external symptoms: the Essential Tremor (ET) and the number of Eye Blinks (EBs). In this paper we present NeuroGlasses, a prototype of sensored glasses able to detect the presence of ET of the head and to count the number of EBs at the same time. To the best of authors' knowledge this is the first attempt to monitor such external symptoms with a transparent and wearable device without any a-priori training. Numerical results prove the reliability of the proposed approach: NeuroGlasses are able to i) correctly recognize the ET with an overall accuracy above 97% and ii) count the number of EBs with a Root Mean Square Error (RMSE) around 0.4.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1063558
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