Implementation, data analysis, and design of unsupervised machine learning methods for the estimation of bed-bound human states, targeting deployment on resource-constrained embedded platforms.

Fabio Salice (mail)

Artificial Intelligence, Architetture a Microcontrollore

Qualità della vita, analisi dei dati, sistemi embedded

Implement a lightweight and contactless sensing system that collects and cleans bed and room sensor data, discovers and models patterns using unsupervised or self-supervised techniques, generates estimators for presence, respiratory rate, and sleep quality indicators, and deploys optimised inference on microcontroller platforms (TinyML).