  Most of today's wearable technology provides seamless cardiac activity
monitoring. Specifically, the vast majority employ Photoplethysmography (PPG)
sensors to acquire blood volume pulse information, which is further analysed to
extract useful and physiologically related features. Nevertheless, PPG-based
signal reliability presents different challenges that strongly affect such data
processing. This is mainly related to the fact of PPG morphological wave
distortion due to motion artefacts, which can lead to erroneous interpretation
of the extracted cardiac-related features. On this basis, in this paper, we
propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System
(IT2FLS) for assessing the quality of PPG signals. The proposed system employs
a personalised approach to adapt the IT2FLS parameters to the unique
characteristics of each individual's PPG signals.Additionally, the system
provides adjustable levels of personalisation, allowing healthcare providers to
adjust the system to meet specific requirements for different applications. The
proposed system obtained up to 93.72\% for average accuracy during validation.
The presented system has the potential to enable ultra-low complexity and
real-time PPG quality assessment, improving the accuracy and reliability of
PPG-based health monitoring systems at the edge.
