Home Automation through Hand Gestures Using ResNet50 and 3D-CNN


  • Ankitha Raksha SRH University Berlin, Charlottenburg, Germany
  • Raghul Krishna Rajasekaran SRH University Berlin, Charlottenburg, Germany
  • Praveen Francis SRH University Berlin, Charlottenburg, Germany
  • Suhas Yogeshwara SRH University Berlin, Charlottenburg, Germany
  • Alexander I. Iliev SRH University Berlin, Charlottenburg, Germany; Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria




Hand Gestures, Home Automation, ResNet50, 3D-CNN


This paper talks about using hand movements for the operations of electrical equipment at home. With the use of the much-advanced algorithms - 3D-CNN and ResNet50 to increase the accuracy in detecting the hand gesture to correctly predict the right motion for the functioning of the electrical device. Eventually, the project focuses on the comparative study between different architectures so that we can determine the best-suited model for these kinds of image detection. We aim to bring about a good accurate model for detecting the hand signals.


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How to Cite

Raksha, A., Krishna Rajasekaran, R., Francis, P., Yogeshwara, S., & I. Iliev, A. (2021). Home Automation through Hand Gestures Using ResNet50 and 3D-CNN. Digital Presentation and Preservation of Cultural and Scientific Heritage, 11, 215–226. https://doi.org/10.55630/dipp.2021.11.18