Project DetailsThis work endeavors to develop a machine learning-based classifier for determining individuals' age status using Photoplethysmography (PPG) signals. Drawing upon findings from numerous academic articles, a validated correlation between age and specific features of PPG signals is established, attributed to variations in arterial health status. Trustworthy PPG datasets are employed for analysis. Using MATLAB, both feature extraction and classifier creation are conducted using its designated tools. Various machine learning classifiers are developed, utilizing the extracted features as inputs. The primary objective is to construct accurate classifiers capable of discerning the age class of patients based on their PPG signals. This research hopes to contribute to advancing non-invasive age assessment methodologies.
