research-article

Predicting the Types of Striking and Thrusting Motions by using Deep Learning

Authors:

Maki NakamuraAuthors Info & Claims

ICBBE '19: Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering

Pages 199 - 202

https://doi.org/10.1145/3375923.3375954

Published: 25 March 2020 Publication History

Get Access

Abstract

The final goal is to make a "tankendo machine" that is easy to use, safe, fun, and can provide a mechanical competitor for humans. First, deep learning technology was used to install an image processing system that could detect the start (initial motion) of striking and thrusting motions by humans and quickly predict the type of technique used.

References

[1]

K.Hoshino and C.Cheng: "Development of a swordsmanship machine enabling the inner and outer muscles to be safely trained while having fun," Proc. 3rd Intl. Conf. on Biomedical and Bioinformatics Engineering, pp.59--62, 2016. http://dx.doi.org/10.1145/3022702.3022711

Digital Library

Google Scholar

[2]

Nippon Budo Kyokai: "Budo: The Martial Ways of Japan," ed. Alexander Bennet, Nippon Budokan Foundation, 2009

Google Scholar

[3]

K.Hoshino: The Method for Analyzing the Motions of Martial Arts. Patent Application No. 2018--27366, Filling date: Feb 19, 2018. (in Japanese)

Google Scholar

[4]

M.Nakamura and K.Hoshino: "Tankendo motion estimation system with robustness against differences in color and size between users' clothes using 4-color markers with elastic belts," Proc. International Conference on Biomedical and Bioinformatics Engineering (ICBBE 2018), pp.110--117, 2018. http://dx.doi.org/10.1145/3301879.3301890

Digital Library

Google Scholar

[5]

K.Hoshino and S.Shimanoe: Face Protector, the Device, and Method for Recording the Motions. Patent Application No. 2018--248113, Filling date: Dec 28, 2018. (in Japanese)

Google Scholar

[6]

S.Shimanoe and K.Hoshino: "A wearable imaging system for recording the game tactics in martial arts using swords," Proc. 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech 2019), pp.139--140, 2019

Crossref

Google Scholar

[7]

M.Nakamura and K.Hoshino: "Experimental Discussion about the Influence of the proficiency in Tankendo on the Foot Posture," Humanities & Social Sciences Reviews, 7, 5, pp 296--305, 2019. https://doi.org/10.18510/hssr.2019.7535

Crossref

Google Scholar

Cited By

View all

Hoshino KOzone YZhao C(2021)A Tankendo Robot that Defends Itself from Opponent Attacks by Moving Immediately before a Valid Strike or ThrustProceedings of the 2021 3rd International Conference on Image, Video and Signal Processing10.1145/3459212.3459230(115-121)Online publication date: 19-Mar-2021
https://dl.acm.org/doi/10.1145/3459212.3459230
Ozone YZhao CHoshino K(2020)Introducing Swordsman Robot DefensingProceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing10.1145/3441369.3441375(33-39)Online publication date: 6-Nov-2020
https://dl.acm.org/doi/10.1145/3441369.3441375

Index Terms

Predicting the Types of Striking and Thrusting Motions by using Deep Learning
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
      1. User interface programming

Recommendations

An Overview of Deep Reinforcement Learning
CACRE2019: Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering

As a new machine learning method, deep reinforcement learning has made important progress in various fields of people's production and life since it was proposed. However, there are still many difficulties in function design and other aspects. Therefore,...
Predicting Student Academic Performance Using Machine Learning
Computational Science and Its Applications – ICCSA 2021
Abstract
The introduction of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data have paved the way for research focused on improving the student learning experience and help to address ...
Robust Deep Reinforcement Learning with Adversarial Attacks
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

This paper proposes adversarial attacks for Reinforcement Learning (RL). These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICBBE '19: Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering

November 2019

214 pages

ISBN:9781450372992

DOI:10.1145/3375923

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

East China Normal University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

the Ministry of Internal Affairs and Communications under SCOPE
Sciences Research Grants of the Ministry of Education, Culture, Sports, Science and Technology

Conference

ICBBE '19

ICBBE '19: 2019 6th International Conference on Biomedical and Bioinformatics Engineering

November 13 - 15, 2019

Shanghai, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
75
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hoshino KOzone YZhao C(2021)A Tankendo Robot that Defends Itself from Opponent Attacks by Moving Immediately before a Valid Strike or ThrustProceedings of the 2021 3rd International Conference on Image, Video and Signal Processing10.1145/3459212.3459230(115-121)Online publication date: 19-Mar-2021
https://dl.acm.org/doi/10.1145/3459212.3459230
Ozone YZhao CHoshino K(2020)Introducing Swordsman Robot DefensingProceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing10.1145/3441369.3441375(33-39)Online publication date: 6-Nov-2020
https://dl.acm.org/doi/10.1145/3441369.3441375

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An Overview of Deep Reinforcement Learning

Predicting Student Academic Performance Using Machine Learning

Robust Deep Reinforcement Learning with Adversarial Attacks