Biomedical sensors and IoT platform utilisation for neurological applications and health event recognitions

Abstract

Biomedical monitoring is becoming a tool which can improve reliable and accurate medical diagnostic and health state evaluation. Mobile software and platforms are hosts for advanced health state monitoring systems, and recently are moving towards decision support tools. The analytical process must be supported by sensing equipment selected to evaluate specific symptoms (and preferably their intensity) of a given disease. This paper will discuss usage of surface multichannel electromyography and supplemented with arms activity evaluation. These data sources need to be selected with regard to not only detection accuracy but also mobility and usability features, which make the measuring process so cumbersome and difficult. In this work we summarize our effort to prepare the sensors, tune them to provide details on NERVE sensor, which has been based on IoT platform components and supplemented with MYO multi-sensor as a supplementary data source. Constructed system has been designed to consume electromyography and inertial data in order to feed seizure detection algorithms, responsible for health event detection and alarms. The application provides also novel approach in case of child seizures aimed not only at supporting parents but also at recording and assessing seizure symptoms. This work demonstrates signal processing algorithms, and describes functionality of designed system in the domain of hardware and software components.