A general skeleton-based action and gesture recognition framework for human–robot collaboration

Terreran, Matteo; Barcellona, Leonardo; Ghidoni, Stefano

doi:10.1016/j.robot.2023.104523

Cited by 10 publications

(1 citation statement)

References 49 publications

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“…The document presents a comprehensive framework for human action and gesture recognition in human-robot collaboration scenarios. [3] It addresses the importance of recognizing human actions for effective and safe collaboration between humans and robots, proposing a framework based on 3D pose estimation and ensemble techniques to recognize both body actions and hand gestures. The proposed framework relies on OpenPose and 2D to 3D lifting methods to estimate 3D joints and uses graph convolutional networks for action recognition.…”

Section: Gesture Recognition For Human-robot Collaborationmentioning

confidence: 99%

Human Action Recognition System Using LRCN

Eldo,

Kuriakose

et al. 2024

IJRASET

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The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

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Section: Gesture Recognition For Human-robot Collaborationmentioning

confidence: 99%