Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network

Moch Iskandar Riansyah; Tri Arief Sardjono; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo

doi:10.1109/ISITIA59021.2023.10221066

Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.

Original language	English
Title of host publication	2023 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publication	Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	99-104
Number of pages	6
ISBN (Electronic)	9798350313956
DOIs	https://doi.org/10.1109/ISITIA59021.2023.10221066
Publication status	Published - 2023
Event	24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023 - Hybrid, Surabaya, Indonesia Duration: 26 Jul 2023 → 27 Jul 2023

Publication series

Name	2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding

Conference

Conference	24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Country/Territory	Indonesia
City	Hybrid, Surabaya
Period	26/07/23 → 27/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

3D Lidar
CNN
Human body
Orientation
Robot

Access to Document

10.1109/ISITIA59021.2023.10221066

Cite this

Riansyah, M. I., Sardjono, T. A., Yuniarno, E. M., & Purnomo, M. H. (2023). Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network. In 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding (pp. 99-104). (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISITIA59021.2023.10221066

Riansyah, Moch Iskandar ; Sardjono, Tri Arief ; Yuniarno, Eko Mulyanto et al. / Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network. 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 99-104 (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding).

@inproceedings{bcd828b8e204408a841beb03ca0d4937,

title = "Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network",

abstract = "Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.",

keywords = "3D Lidar, CNN, Human body, Orientation, Robot",

author = "Riansyah, \{Moch Iskandar\} and Sardjono, \{Tri Arief\} and Yuniarno, \{Eko Mulyanto\} and Purnomo, \{Mauridhi Hery\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023 ; Conference date: 26-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1109/ISITIA59021.2023.10221066",

language = "English",

series = "2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2023 International Seminar on Intelligent Technology and Its Applications",

address = "United States",

}

Riansyah, MI, Sardjono, TA , Yuniarno, EM & Purnomo, MH 2023, Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network. in 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding, Institute of Electrical and Electronics Engineers Inc., pp. 99-104, 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023, Hybrid, Surabaya, Indonesia, 26/07/23. https://doi.org/10.1109/ISITIA59021.2023.10221066

Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network. / Riansyah, Moch Iskandar; Sardjono, Tri Arief ; Yuniarno, Eko Mulyanto et al.
2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. p. 99-104 (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network

AU - Riansyah, Moch Iskandar

AU - Sardjono, Tri Arief

AU - Yuniarno, Eko Mulyanto

AU - Purnomo, Mauridhi Hery

PY - 2023

Y1 - 2023

N2 - Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.

AB - Robot interaction with humans requires intelligent robots that can understand human activities. The development of advanced 3D LiDAR sensors has greatly contributed to this capability. In this study, we specifically focus on the use of 3D LiDAR sensors to predict the orientation of the human body using 3D Convolutional Neural Networks (CNNs) based on voxelized datasets. The dataset used in this study was created using a 3D LiDAR sensor with 32-channel specifications. We divided the dataset into four categories representing different walking orientations. The goal was to explore the performance of four different 3D CNN architectures using independently generated datasets. Based on the experimental results and performance analysis, it was found that VGG16 outperformed the other three architectures in predicting body orientation. VGG16 achieved an accuracy of 0.95, which was higher than DenseNet121 with approximately 0.91, ResNet50V2 with 0.80, and ResNet50 with 0.73. In the future, this method will be developed with additional orientation and results of architectural testing so that it can be modified to be better for further research on understanding human activity by robots.

KW - 3D Lidar

KW - CNN

KW - Human body

KW - Orientation

KW - Robot

UR - https://www.scopus.com/pages/publications/85171130496

U2 - 10.1109/ISITIA59021.2023.10221066

DO - 10.1109/ISITIA59021.2023.10221066

M3 - Conference contribution

AN - SCOPUS:85171130496

T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding

SP - 99

EP - 104

BT - 2023 International Seminar on Intelligent Technology and Its Applications

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

Y2 - 26 July 2023 through 27 July 2023

ER -

Riansyah MI, Sardjono TA , Yuniarno EM , Purnomo MH. Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network. In 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2023. p. 99-104. (2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding). doi: 10.1109/ISITIA59021.2023.10221066

Prediction of Human Body Orientation based on Voxel Using 3D Convolutional Neural Network

Abstract

Publication series

Conference

UN SDGs

Keywords

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