skip to main content
10.1145/3132818.3132830acmconferencesArticle/Chapter ViewAbstractPublication Pagessiggraph-asiaConference Proceedingsconference-collections
abstract

Computational foresight: realtime forecast of human body motion

Published: 27 November 2017 Publication History

Abstract

In this paper, we propose a machine learning-based system named "Computational Foresight" that can forecast body motion 0.5 seconds before. The system detects 25 human body joints by Kinect V2. We created 5-layered neural network system for machine-learning. We achieved real-time motion forecast system. This can be used to estimate human gesture inputs in advance, instruct sports actions properly, and prevent elderly from falling to the ground, and so on.

Supplementary Material

ZIP File (a2-horiuchi.zip)
Supplemental material.
MP4 File (a2-horiuchi.mp4)

References

[1]
Mohammad Bataineh, Timothy Marler, Karim Abdel-Malek, and Jasbir Arora. 2016. Neural network for dynamic human motion prediction. Expert Systems with Applications 48 (2016), 26--34.
[2]
Chen Chen, Kui Liu, and Nasser Kehtarnavaz. 2016. Real-time human action recognition based on depth motion maps. Journal of real-time image processing 12, 1 (2016), 155--163.
[3]
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. 2014. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36, 7 (2014), 1325--1339.
[4]
Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. 2016. Structural-RNN: Deep learning on spatio-temporal graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5308--5317.
[5]
Julieta Martinez, Michael J Black, and Javier Romero. 2017. On human motion prediction using recurrent neural networks. arXiv preprint arXiv:1705.02445 (2017).
[6]
Seong-Bin Park, Sa-Yup Kim, Joon-Ho Hyeong, and Kyung-Ryul Chung. 2014. A study on the development of image analysis instrument and estimation of mass, volume and center of gravity using CT image in Korean. Journal of Mechanical Science and Technology 28, 3 (2014), 971.
[7]
Jack M Wang, David J Fleet, and Aaron Hertzmann. 2008. Gaussian process dynamical models for human motion. IEEE transactions on pattern analysis and machine intelligence 30, 2 (2008), 283--298.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SA '17: SIGGRAPH Asia 2017 Emerging Technologies
November 2017
30 pages
ISBN:9781450354042
DOI:10.1145/3132818
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 November 2017

Check for updates

Author Tags

  1. center of gravity
  2. regression

Qualifiers

  • Abstract

Conference

SA '17
Sponsor:
SA '17: SIGGRAPH Asia 2017
November 27 - 30, 2017
Bangkok, Thailand

Acceptance Rates

Overall Acceptance Rate 178 of 869 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 296
    Total Downloads
  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)1
Reflects downloads up to 03 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media