Matteo Ricci

PhD Student
Electronic Engineering Department
Universitat Politècnica de València (Spain)

Research line: Biosignal Analysis
PhD Supervisor: José J. Rieta

Doctoral Thesis

Implementation of Efficient Algorithms for PPG Signal Preprocessing in Wearable Devices

This doctoral research focuses on improving the quality of photoplethysmography (PPG) signals recorded by wearable devices. These optical signals support noninvasive physiological monitoring, but motion, ambient light, and sensor placement can introduce noise and distort the pulse waveform. The goal is to develop robust preprocessing algorithms that reduce these disturbances while preserving the information needed for subsequent analysis.

The research plan combines signal processing and adaptive filtering with machine learning methods for detecting and correcting motion artifacts. It will evaluate techniques such as wavelet transforms and empirical mode decomposition, using PPG recordings alongside accelerometer data to distinguish clean from corrupted signal segments. A central requirement is computational efficiency, so the methods can operate in real time on wearable devices with limited processing power and energy resources.

The proposed algorithms will be evaluated using data collected in controlled laboratory conditions and during everyday activities. Validation will consider signal quality, noise reduction, processing latency, and the effect of preprocessing on physiological feature extraction. The aim is to make wearable PPG measurements more reliable without imposing excessive computational demands. This description summarizes the doctoral research plan and its objectives.