The leading trends in the framework of the Internet of Things are driving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. One of the major opportunities offered by IoT lies in the ubiquitous connectivity, thus enabling smart services such as remote patient monitoring, in-home therapy/rehabilitation, and assisted living platforms. In this paper we present a prototype of wearable smart glasses able to monitor the Eye Blinks (EBs) through ElectroOculoGram (EOG) signal in a transparent way with respect to the final user. We propose a novel pre-filtering scheme to reduce EOG noise along with an analytical derivation of a matched filter to detect and count EBs. We have carried out an in-depth experimental campaign in order to validate the robustness of our approach with respect to the main solutions available in the literature. Furthermore, we have compared the performances obtained with out wearable prototype versus the results achievable with professional medical equipments. Results show that our solution is able to achieve very high accuracy in EB detection, obtaining comparable performance with respect to professional medical desktop equipment, with the additional benefit of portability, comfort and easiness of use for the patients.

Leveraging IoT Wearable Technology towards Early Diagnosis of Neurological Diseases

Sciarrone A.;Bisio I.;Garibotto C.;Lavagetto F.;
2021

Abstract

The leading trends in the framework of the Internet of Things are driving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. One of the major opportunities offered by IoT lies in the ubiquitous connectivity, thus enabling smart services such as remote patient monitoring, in-home therapy/rehabilitation, and assisted living platforms. In this paper we present a prototype of wearable smart glasses able to monitor the Eye Blinks (EBs) through ElectroOculoGram (EOG) signal in a transparent way with respect to the final user. We propose a novel pre-filtering scheme to reduce EOG noise along with an analytical derivation of a matched filter to detect and count EBs. We have carried out an in-depth experimental campaign in order to validate the robustness of our approach with respect to the main solutions available in the literature. Furthermore, we have compared the performances obtained with out wearable prototype versus the results achievable with professional medical equipments. Results show that our solution is able to achieve very high accuracy in EB detection, obtaining comparable performance with respect to professional medical desktop equipment, with the additional benefit of portability, comfort and easiness of use for the patients.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1063550
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