Deep Learning-based human activity recognition using RGB images in Human-robot collaboration

Abstract

In human-robot interaction, to ensure safety and effectiveness, robots need to be able to accurately predict human intentions. Hidden Markov Model, Bayesian Filtering, and deep learning methods have been used to predict human intentions. However, few studies have explored deep learning methods to predict variant human intention. Our study aims to evaluate the performance of the human intent recognition inference algorithm, and its impact on the human-robot team for collaborative tasks. Two deep learning algorithms ConvLSTM and LRCN were used to predict human intention. A dataset of 10 participants performing Pick, Throw, Wave, and Carry actions was used. The ConvLSTM method had a prediction accuracy of 74%. The LRCN method had a lower prediction accuracy of 25% compared to ConvLSTM. This result shows that deep learning methods using RGB images can predict human intent with high accuracy. The proposed method is successful in predicting human intents underlying human behavior.

An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system

“…The accuracy of various classifiers' model classifications was stated to be 58%, 84%, 86%, and 90%. While deep learningbased human activity recognition in human-robot cooperation utilizing RGB photos is described in other studies 13 . To anticipate human intention, two deep learning algorithms, ConvLSTM and LRCN, were utilized.…”

“…Several academics are interested in the process of identifying and recognizing the kinds of human movement because of its significance and requirement for such systems [12][13][14] . Academics are interested in the usage of wireless equipment networks because of the numerous applications for identifying human physical motions, including healthcare, rehabilitation, athletics, elder monitoring, sports, and human-robot interaction [15][16][17] .…”

Classification of Human Driver Distraction Using 3D Convolutional Neural Networks

“…Convolutional neural networks (2-Dimentional) have shown promise for driver distraction classification due to their ability to effectively capture spatial features of the input data (Keshinro et al, 2022). However, 2D-CNNs are limited in their ability to capture temporal features of driver behavior data, which can be important for accurately detecting driver distraction.…”