User Digitization

User digitization aims to create comprehensive digital representations of individuals, capturing diverse aspects like activity, physiology, and behavior, to enable more personalized and effective computing experiences. Current research focuses on developing practical and high-fidelity sensing systems, employing machine learning models such as convolutional neural networks, autoencoders, and transformers, along with Bayesian approaches for improved accuracy and efficiency. This field is significant for advancing applications in healthcare, cultural heritage preservation, and industrial automation, while also raising important ethical considerations regarding privacy and data security.