WATCHDOG: Autonomous Elderly Assistance Via Attention-Based Fall Detection and Trajectory Prediction

Abstract

Service robots designed to assist elderly people are receiving significant attention since they can improve their quality of life, promote their independence, and provide daily support. These mobile platforms can observe people moving around their homes, recognise dangerous events, and detect them promptly. This paper introduces a novel framework to perform fall detection and people following on board an autonomous legged robotic platform. The system operates on the Unitree Go2 robot and comprises two main building blocks. The first component consists of a Body Landmarks extractor and a Transformer-based network that performs binary classification, distinguishing between Fall behaviours and Activities of Daily Living (ADL). The second component is a target-driven path planner that enables the robot to follow and maintain a full-body view of the target in complex environments. Experiments on public datasets and comparison with state-of-the-art works have been conducted to demonstrate the reliability of both blocks. Real experiments in a cluttered environment have been performed to illustrate how the mobile platform is able to follow people moving around obstacles, detect falls in occluded areas, and predict people’s trajectories to maintain a full-body view.