Project DetailsThe primary objective of this project is to predict biological characteristics of subjects, such as age and gender, through the analysis of Photoplethysmography (PPG) signals. The PPG signal serves as a non-invasive physiological indicator that reflects changes in blood volume within tissues and is commonly used as a research and clinical tool for assessing various health parameters. In this project, we used a deep learning model that received preprocessed PPG signals as input and produced estimations of each subject’s biological characteristics as output. However, to further improve the model’s performance and enhance its generalization capability, future work should focus on expanding the datasets to include larger, more diverse samples with a broader range of biological attributes. The integration of such datasets, along with the exploration of more advanced network architectures, has the potential to significantly improve the accuracy and reliability of the predictions.
