Allassan Tchangmena A Nken

Title:  Multimodal Federated Learning Approach for Human Activity Recognition in Privacy Preserving Videos (FLARE)

Supervision Team: Ihsan Ullah, UoG / Susan Mckeever, TU Dublin / Michael Schukat, UoG and Peter Corcoran, UoG

Description: The United Nations reported a rapid rise in the numbers of people living well beyond retirement age. Older adults wish to maintain a high-quality independent lifestyle without the need for high-cost medical/care interventions. Several technology-based solutions use machine-learning e.g., human activity recognition (HAR) systems which focus on monitoring pure health conditions, but it is largely known that wellbeing is a much more subjective and complex concept. Recent state-of-the-art machine-learning algorithms are trained on large amounts of centrally stored data which is hard for various reasons e.g., privacy loss, load on network while data transfer, General-data-protection-rules restrictions. More specifically, due to privacy concerns such solutions face acceptability barriers because of being considered too invasive. This project aims to address acceptability problem and better results in HAR via the use of by default privacy preserving imaging types (e.g., non-RGB (face unrecognisable)) and federated learning approach (data remains at owner place).