Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing
This study aims at sensing and understanding the worker's activity in a human-centered intelligent manufacturing system. We propose a novel multi-modal approach for worker activity recognition by leveraging information from different sensors and in different modalities. Specifically, a smart armband and a visual camera are applied to capture Inertial Measurement Unit (IMU) signals and videos, respectively. For the IMU signals, we design two novel feature transform mechanisms, in both frequency and spatial domains, to assemble the captured IMU signals as images, which allow using convolutional neural networks to learn the most discriminative features. Along with the above two modalities, we propose two other modalities for the video data, i.e., at the video frame and video clip levels. Each of the four modalities returns a probability distribution on activity prediction. Then, these probability distributions are fused to output the worker activity classification result. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab a tool/part, hammer a nail, use a power-screwdriver, rest arms, turn a screwdriver, and use a wrench. The developed multi-modal approach is evaluated on this dataset and achieves recognition accuracies as high as 97% and 100% in the leave-one-out and half-half experiments, respectively.
W. Tao et al., "Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing," Engineering Applications of Artificial Intelligence, vol. 95, Elsevier, Oct 2020.
The definitive version is available at https://doi.org/10.1016/j.engappai.2020.103868
Mechanical and Aerospace Engineering
Keywords and Phrases
Deep learning; Human-centered computing; Intelligent manufacturing; Multi-modal fusion; Worker activity recognition
International Standard Serial Number (ISSN)
Article - Journal
© 2020 International Federation of Automatic Control (IFAC) , All rights reserved.
01 Oct 2020