Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

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Resumen

This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5% of accuracy and a loss of 3.5% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2% accuracy and a margin of error of 1.8% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.

Idioma originalInglés
Título de la publicación alojada2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665458924
DOI
EstadoPublicada - 2022
Evento2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 - Ixtapa, México
Duración: 9 nov. 202211 nov. 2022

Serie de la publicación

Nombre2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

Conferencia

Conferencia2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
País/TerritorioMéxico
CiudadIxtapa
Período9/11/2211/11/22

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