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.

翻译：当今大多数可穿戴技术能够实现无缝的心脏活动监测。具体而言，绝大多数设备采用光电容积描记（PPG）传感器获取血容量脉搏信息，并进一步分析以提取有用且与生理相关的特征。然而，基于PPG的信号可靠性面临多种挑战，这些挑战严重影响此类数据处理。这主要源于运动伪影导致的PPG形态波畸变，可能引发对提取的心脏相关特征产生错误解读。基于此，本文提出一种新颖的个性化可调区间二型模糊逻辑系统（IT2FLS），用于评估PPG信号质量。该系统采用个性化方法，将IT2FLS参数适配至每位个体PPG信号的独特特征。此外，系统提供可调个性化程度，允许医疗保健提供者根据不同应用的具体需求调整系统。验证期间，所提系统平均准确率高达93.72%。该系统有望实现超低复杂度与实时PPG质量评估，从而提升边缘端基于PPG的健康监测系统的准确性与可靠性。