Application of Convolutional Neural Networks to Emotion Recognition for Robotic Arm Manipulation

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Resumen

This paper presents the development of a system that operates a robotic arm to deliver an object based on the facial expression of a human standing in front of the robot, demonstrating real-Time emotion recognition for physical Human-Robot Interaction. To achieve this, a convolutional neural network-based model was developed to identify emotions in real time. The robotic arm operation was implemented using an embedded NVidia Jetson Nano computer, a web camera, and OpenCV, ROS, and TensorFlow libraries. Using a 26.6k face photos data set from the emotion detection database, the built emotion detection model demonstrated an accuracy of 93.5% and an error of 6.5% during training and validation. The final real-Time prototype had a testing accuracy of 94% with an error of 6%. This proof-of-concept shows that in the near future more advanced applications that harness user emotions may also be built.

Idioma originalInglés
Título de la publicación alojada2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings
EditoresAlvaro David Orjuela-Canon
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798350316599
DOI
EstadoPublicada - 26 jul. 2023
Evento2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia
Duración: 26 jul. 202328 jul. 2023

Serie de la publicación

Nombre2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

Conferencia

Conferencia2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
País/TerritorioColombia
CiudadBogota
Período26/07/2328/07/23

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