Skeleton-Based Bio-Inspired Human Activity Prediction for Real-Time Human-Robot Interaction
Activity prediction is an essential task in practical human-centered robotics applications, such as security, assisted living, etc., which is targeted at inferring ongoing human activities based on incomplete observations. To address this challenging problem, we introduce a novel bio-inspired predictive orientation decomposition (BIPOD) approach to construct representations of people from 3D skeleton trajectories. BIPOD is invariant to scales and viewpoints, runs in real-time on basic computer systems, and is able to recognize and predict activities in an online fashion. Our approach is inspired by biological research in human anatomy. To capture spatio-temporal information of human motions, we spatially decompose 3D human skeleton trajectories and project them onto three anatomical planes (i.e., coronal, transverse and sagittal planes); then, we describe short-term time information of joint motions and encode high-order temporal dependencies. By using Extended Kalman Filters to estimate future skeleton trajectories, we endow our BIPOD representation with the critical capabilities to reduce noisy skeleton observation data and predict the ongoing activities. Experiments on benchmark datasets have shown that our BIPOD representation significantly outperforms previous methods for real-time human activity classification and prediction from 3D skeleton trajectories. Empirical studies using TurtleBot2 and Baxter humanoid robots have also validated that our BIPOD method obtains promising performance, in terms of both accuracy and efficiency, making BIPOD a fast, simple, yet powerful representation for low-latency online activity prediction in human–robot interaction applications.
Citation: Skeleton-Based Bio-Inspired Human Activity Prediction for Real-Time Human-Robot Interaction. Brian Reily, fei Han, Lynne Parker, and Hao Zhang. Autonomous Robots (AuRo) vol. 42, pp. 1281-1298, 2018.