The increasing labor shortage and aging population underline the need for assistive robots to support human care recipients. To enable safe and responsive assistance, robots require accurate human motion prediction in physical interaction scenarios. However, this remains a challenging task due to the variability of assistive settings and the complexity of coupled dynamics in physical interactions. In this work, we address these challenges through two key contributions: (1) HHI-Assist, a dataset comprising motion capture clips of human-human interactions in assistive tasks; and (2) a conditional Transformer-based denoising diffusion model for predicting the poses of interacting agents. Our model effectively captures the coupled dynamics between caregivers and care receivers, demonstrating improvements over baselines and strong generalization to unseen scenarios. By advancing interaction-aware motion prediction and introducing a new dataset, our work has the potential to significantly enhance robotic assistance policies. The dataset and code are available at: https://sites.google.com/view/hhi-assist/home

Unpaid caregivers for persons with cognitive impairments provide a valuable medical service at a personal cost to themselves (both health and financial). Smart systems in the home can potentially ease the caregiver burden but the home is a difficult environment for smart systems to operate. This work in progress examines the design of a smart caregiver support system and how it is extended in a new system to support working caregivers. The system uses AI in a human-in-the-loop approach.