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Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

Human Robotics Group, University of Alicante, San Vicente del Raspeig, 03690 Alicante, Spain
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Academic Editors: Alessandro Di Nuovo and Manuel Armada
Appl. Sci. 2021, 11(9), 4183; https://doi.org/10.3390/app11094183 (registering DOI)
Received: 26 March 2021 / Revised: 17 April 2021 / Accepted: 30 April 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Robotic Platforms for Assistance to People with Disabilities)
Assistance and rehabilitation robotic platforms must have precise sensory systems for human–robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human–robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications. View Full-Text
Keywords: human–robot interaction; human pose estimation; robotic rehabilitation human–robot interaction; human pose estimation; robotic rehabilitation
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MDPI and ACS Style

Hernández, Ó.G.; Morell, V.; Ramon, J.L.; Jara, C.A. Human Pose Detection for Robotic-Assisted and Rehabilitation Environments. Appl. Sci. 2021, 11, 4183. https://doi.org/10.3390/app11094183

AMA Style

Hernández ÓG, Morell V, Ramon JL, Jara CA. Human Pose Detection for Robotic-Assisted and Rehabilitation Environments. Applied Sciences. 2021; 11(9):4183. https://doi.org/10.3390/app11094183

Chicago/Turabian Style

Hernández, Óscar G.; Morell, Vicente; Ramon, José L.; Jara, Carlos A. 2021. "Human Pose Detection for Robotic-Assisted and Rehabilitation Environments" Appl. Sci. 11, no. 9: 4183. https://doi.org/10.3390/app11094183

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