short-paper

Hand motion capture system based on multiple inertial sensors: demo abstract

Authors:

Chenghong Lu,

Jiangkun Wang,

Lei JingAuthors Info & Claims

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

Pages 619 - 620

https://doi.org/10.1145/3384419.3430461

Published: 16 November 2020 Publication History

Get Access

Abstract

It is important for many applications to capture hand movements with high accuracy to achieve the natural human-computer interaction, such as games, robotics, rehabilitation, and virtual reality (VR). An ideal hand motion capture solution requires good mobility, unobtrusiveness, and high accuracy. In this demo, we show a hand motion capture system including inertial sensor based data gloves with the square-root cubature Kalman Filter multi-sensor fusion algorithm and a biomechanics sensor-to-segment calibration method. The absolute error of the joint angle is measured. As the result, the proposed system shows good accuracy in both static (RMSE = 1.5°) and dynamic (RMSE = 6.6°) conditions.

References

[1]

Arasaratnam, I., and Haykin, S. Cubature kalman filters. IEEE Transactions on Automatic Control 54 (2009), 1254--1269.

Crossref

Google Scholar

[2]

Connolly, J., Condell, J., O'Flynn, B., Sanchez, J. T., and Gardiner, P. Imu sensor-based electronic goniometric glove for clinical finger movement analysis. IEEE Sensors Journal 18 (2018), 1273--1281.

Google Scholar

[3]

Rashid, A., and Hasan, O. Wearable technologies for hand joints monitoring for rehabilitation: A survey. Microelectron. J. 88 (2019), 173--183.

Crossref

Google Scholar

[4]

Salchow-Hömmen, C., Callies, L., Laidig, D., Valtin, M., Schauer, T., and Seel, T. A tangible solution for hand motion tracking in clinical applications. Sensors (Basel, Switzerland) 19 (2019).

Google Scholar

[5]

Vitali, R. V., and Perkins, N. C. Determining anatomical frames via inertial motion capture: A survey of methods. Journal of Biomechanics 106 (2020), 109832.

Crossref

Google Scholar

Cited By

View all

Chandana BKanth MAnand GJagdish M(2024)Transforming Teaching and Learning with Hand Gestures2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10688063(1-5)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10688063
Zhao XCui SZhang ZCao QYuan YWu XZheng S(2023)Motion Capture Modeling of Dexterous Hand for Intelligent SensingAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7593-8_28(329-342)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7593-8_28
Wang JJing L(2022)A Virtual Reality Whiteboard System for Remote Collaboration Using Natural HandwritingElectronics10.3390/electronics1124415211:24(4152)Online publication date: 12-Dec-2022
https://doi.org/10.3390/electronics11244152

Index Terms

Hand motion capture system based on multiple inertial sensors: demo abstract
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Motion capture
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor devices and platforms

Recommendations

Hand motion capture for medical usage
ICBBE '16: Proceedings of the 3rd International Conference on Biomedical and Bioinformatics Engineering

The authors propose, not a non-contact devise which our research group has been developing, but a compact wearable device which allows for estimation of the hand pose (hand motion capture) of the subject. The devise has a miniature wireless RGB camera ...
Modeling the constraints of human hand motion
HUMO '00: Proceedings of the Workshop on Human Motion (HUMO'00)

Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-...
Comparing Real-Time Human Motion Capture System Using Inertial Sensors with Microsoft Kinect
U-MEDIA '14: Proceedings of the 2014 7th International Conference on Ubi-Media Computing and Workshops

Research in Human-Computer Interaction (HCI) has gained interest in recent years and has fostered new ideas and expectations. There are many kinds of human motion capture methods nowadays. In this paper, we have compared 2 kinds of methods which are the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems

November 2020

852 pages

ISBN:9781450375900

DOI:10.1145/3384419

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

JSPS KAKENHI

Conference

SenSys '20

Sponsor:

SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems

November 16 - 19, 2020

Virtual Event, Japan

Acceptance Rates

Overall Acceptance Rate 174 of 867 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
177
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)4

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chandana BKanth MAnand GJagdish M(2024)Transforming Teaching and Learning with Hand Gestures2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10688063(1-5)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10688063
Zhao XCui SZhang ZCao QYuan YWu XZheng S(2023)Motion Capture Modeling of Dexterous Hand for Intelligent SensingAdvanced Computational Intelligence and Intelligent Informatics10.1007/978-981-99-7593-8_28(329-342)Online publication date: 30-Oct-2023
https://doi.org/10.1007/978-981-99-7593-8_28
Wang JJing L(2022)A Virtual Reality Whiteboard System for Remote Collaboration Using Natural HandwritingElectronics10.3390/electronics1124415211:24(4152)Online publication date: 12-Dec-2022
https://doi.org/10.3390/electronics11244152

View Options

Get Access

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

Hand motion capture for medical usage

Modeling the constraints of human hand motion

Comparing Real-Time Human Motion Capture System Using Inertial Sensors with Microsoft Kinect