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.

中文翻译：

---

利用物联网可穿戴技术进行神经疾病的早期诊断

物联网框架的领先趋势正在推动研究界提供旨在彻底改变医学科学和医疗保健的智能系统和解决方案。物联网提供的主要机会之一在于无处不在的连接性，从而实现远程患者监护、家庭治疗/康复和辅助生活平台等智能服务。在本文中，我们展示了一种可穿戴智能眼镜的原型，它能够通过眼电图 (EOG) 信号以相对于最终用户透明的方式监控眨眼 (EB)。我们提出了一种新颖的预滤波方案，以减少 EOG 噪声以及匹配滤波器的分析推导，以检测和计数 EB。我们进行了深入的实验活动，以验证我们的方法相对于文献中可用的主要解决方案的稳健性。此外，我们比较了使用可穿戴原型获得的性能与使用专业医疗设备获得的结果。结果表明，我们的解决方案能够在 EB 检测中实现非常高的准确度，获得与专业医疗桌面设备相当的性能，并具有便携性、舒适性和患者易用性的额外优势。