<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The cognitive goal of this paper is to assess whether marker-less motion capture systems provide sufficient data to recognize human postures in the side view. The research goal is to develop a new posture classification method that allows for analysing human activities using data recorded by RGB-D sensors. The method is insensitive to recorded activity duration and gives satisfactory results for the sagittal plane. An improved competitive Neural Network (cNN) was used. The method of preprocessing the data is first discussed. Then, a method for classifying human postures is presented. Finally, classification quality using various distance metrics is assessed. The data sets covering the selection of human activities have been created. Postures typical for these activities have been identified using the classifying neural network. The classification quality obtained using the proposed cNN network and two other popular neural networks were compared. The results confirmed the advantage of cNN network. The developed method makes it possible to recognize human postures by observing movement in the sagittal plane.</p>
</abstract>

The cognitive goal of this paper is to assess whether marker-less motion capture systems provide sufficient data to recognize human postures in the side view. The research goal is to develop a new posture classification method that allows for analysing human activities using data recorded by RGB-D sensors. The method is insensitive to recorded activity duration and gives satisfactory results for the sagittal plane. An improved competitive Neural Network (cNN) was used. The method of preprocessing the data is first discussed. Then, a method for classifying human postures is presented. Finally, classification quality using various distance metrics is assessed. The data sets covering the selection of human activities have been created. Postures typical for these activities have been identified using the classifying neural network. The classification quality obtained using the proposed cNN network and two other popular neural networks were compared. The results confirmed the advantage of cNN network. The developed method makes it possible to recognize human postures by observing movement in the sagittal plane.

Improved Competitive Neural Network for Classification of Human Postures Based on Data from RGB-D Sensors

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The cognitive goal of this paper is to assess whether marker-less motion capture systems provide sufficient data to recognize human postures in the side view....

Improved Competitive Neural Network for Classification of Human Postures Based on Data from RGB-D Sensors

Publié en ligne: 04 mars 2024

Pages: 15 - 28

Reçu: 05 janv. 2023

Accepté: 18 avr. 2023

DOI: https://doi.org/10.14313/jamris/3-2023/19

Mots clés
Human motion, Posture classification, Human activity, Competitive neural network, Classifier

© 2023 Vibekananda Dutta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improved Competitive Neural Network for Classification of Human Postures Based on Data from RGB-D Sensors

Publié en ligne: 04 mars 2024

Pages: 15 - 28

Reçu: 05 janv. 2023

Accepté: 18 avr. 2023

DOI: https://doi.org/10.14313/jamris/3-2023/19

Mots clésHuman motion, Posture classification, Human activity, Competitive neural network, Classifier

© 2023 Vibekananda Dutta et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Human motion, Posture classification, Human activity, Competitive neural network, Classifier