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Computer Science > Human-Computer Interaction

arXiv:2309.13464 (cs)
[Submitted on 23 Sep 2023]

Title:Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge

Authors:Jose A. Miranda, Celia López-Ongil, Javier Andreu-Perez
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Abstract: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 this http URL, 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.
Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2309.13464 [cs.HC]
  (or arXiv:2309.13464v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2309.13464
Journal reference: 2023 IEEE International Conference on Fuzzy Systems (FUZZ 2023)

Submission history

From: Javier Andreu-Perez Dr [view email]
[v1] Sat, 23 Sep 2023 19:35:00 UTC (2,850 KB)
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