Towards Interval Type-2 Fuzzy-Based PPG Quality Assessment for Physiological Monitoring

Wearable technology is having a profound impact in healthcare applications. In this context, one of the main goals of current wearable systems is to provide new devices capable of delivering a greater amount of more precise information. Amongst the different applications, physiological continuous monitoring is becoming one of the most wanted features. In fact, most of the commercially and research grade wearable systems include cardiac activity acquisition, which is generally performed by means of photoplethysmography sensors (PPG). These optical-based sensors present different challenges related to the prevailing of a good signal quality. Thus, in case of dealing with digital processing algorithms which are responsible for extracting different features from such signal, this fact can lead to erroneous results. On this basis, this paper presents an ongoing work towards the design of an interval type-II fuzzy-based system for the PPG signal quality assessment. Moreover, this initial proposed system is implemented into a constrained 32-bit ARM Cortex-M4 system-on-chip. Specifically, the system uses a reduced set of features together with a low complexity fuzzy rule base Mamdani inference model, and is based on a non-overlapping 3-second signal processing window. Results show that the system achieved overall accuracy of 94.84%. The proposed system has great potential for integrating accurate and reliable continuous health monitoring systems into constrained edge devices.