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Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

Journal of Robotics and Control (JRC) · Vol. 4 No. 6 (2023) · hlm. 856–867

DOI
10.18196/jrc.v4i6.20399
ISSN
2715-5072, 2715-5056
Terbit
2023-12-13
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

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26 penulis · 73 tautan kepenulisan bersama · 42 lintas organisasi

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  • Universitas Trunodjoyo Madura
  • State University of Semarang
  • Universitas Muhammadiyah Gresik
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Universitas Mandala Waluya
  • Muhammadiyah University of Yogyakarta
  • Universitas Ahmad Dahlan
  • Organisasi lain
  • Organisasi berbeda
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  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
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Rekan penulisOrganisasiArtikel bersama
Ach. DafidUniversitas Trunodjoyo Madura2
Wahyu CaesarendraUniversiti Brunei Darussalam2
Alfian Ma’arifUniversitas Ahmad Dahlan2
Iswanto SuwarnoMuhammadiyah University of Yogyakarta2
Harunur RosyidUniversitas Muhammadiyah Gresik1
Amelia WidihastutikState University of Semarang1
Girindra Putri Dewi SaraswatiState University of Semarang1
Nurhusna KamilSunan Kalijaga State Islamic University Yogyakarta1
Kalilah NarjisSunan Kalijaga State Islamic University Yogyakarta1
Hairil BudiartoUniversitas Trunodjoyo Madura1
Vivi TriwidyaningrumUniversitas Trunodjoyo Madura1
Adi KurniawanUniversitas Trunodjoyo Madura1
Rajermani ThinakaranINTI International University1
Hanifudin SukriUniversitas Trunodjoyo Madura1
Achmad Fiqhi IbadillahUniversitas Trunodjoyo Madura1
Md. Monzur MorshedUniversity of Dhaka1
Denni KurniawanUniversiti Teknologi Brunei1
Islaeli IslaeliUniversitas Mandala Waluya1
Mimi YatiUniversitas Mandala Waluya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network</dc:title>
	<dc:creator>Fuad, Muhammad</dc:creator>
	<dc:creator>Umam, Faikul</dc:creator>
	<dc:creator>Wahyuni, Sri</dc:creator>
	<dc:creator>Fahriani, Nuniek</dc:creator>
	<dc:creator>Nurwahyudi, Ilham</dc:creator>
	<dc:creator>Darwaman, Mochammad Ilham</dc:creator>
	<dc:creator>Maulana, Fahmi</dc:creator>
	<dc:subject xml:lang="en-US">Gesture</dc:subject>
	<dc:subject xml:lang="en-US">Upper Human Body</dc:subject>
	<dc:subject xml:lang="en-US">Convolutional Neural Network</dc:subject>
	<dc:subject xml:lang="en-US">Mobile Robot Control.</dc:subject>
	<dc:description xml:lang="en-US">Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2023-12-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/20399</dc:identifier>
	<dc:identifier>10.18196/jrc.v4i6.20399</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 4 No. 6 (2023); 856-867</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v4i6</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/20399/8805</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Muhammad Fuad, Faikul Umam, Sri Wahyuni, Nuniek Fahriani, Ilham Nurwahyudi, Mochammad Ilham Darwaman, Fahmi Maulana</dc:rights>
</oai_dc:dc>