ArticleSensors (Basel, Switzerland)2023
Multi-Camera-Based Human Activity Recognition for Human-Robot Collaboration in Construction.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
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- Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis.Scientific reports · 2025Article
- Memory-Augmented 3D Point Cloud Semantic Segmentation Network for Intelligent Mining Shovels.Sensors (Basel, Switzerland) · 2024Article
- Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human-robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human-robot collaboration in construction.
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Registered trials
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