Toward fast 3D human activity recognition: A refined feature based on minimum joint freedom model (Mint)

Abstract

Human activity recognition (HAR) is crucial for human-centric smart manufacturing, especially the management of human involved production activities. Recently, 3D HAR based on skeleton data has emerged as an outstanding approach for good performance on both speed and accuracy. However, there are still flaws like insufficiency or redundancy in design of features. To address this gap, a refined skeleton-based feature for fast and accurate 3D HAR is proposed in this work, Specifically, a minimum and lossless skeleton feature named Mint is proposed based on minimum joint freedom model, with a reduction of around 52% skeleton data dimensions to classic coordinate-based feature. For demonstration, several comparison experiments are implemented based on the presented feature and classic coordinate-based feature. Results show that the utilization of proposed feature can improve the efficiency of 3D HAR around 29% with equivalent accuracy. It is expected this work can contribute a lot to the application of HAR in human-centric smart manufacturing systems and real-time human activity digital twin (HADT) modelling and interaction.