skip to main content
10.1145/2160125.2160131acmotherconferencesArticle/Chapter ViewAbstractPublication PagesahConference Proceedingsconference-collections
research-article

A method to evaluate metal filing skill level with wearable hybrid sensor

Published: 08 March 2012 Publication History

Abstract

This paper presents a method to evaluate a person's skill level for metal filing. Metal filing by expert engineers is an important manufacturing skill that supports basic areas of industry, although most sequences are already automated with industrial robots.
However, there is no effective training method for the skill; "coaching" has been most weighted. Most coaching has depended on the coaches' personal viewpoints. In addition, skill levels have been assessed subjectively by the coaches. Because of these problems, learners have to spend several hundred hours to acquire the basic manufacturing skill.
Therefore, to develop an effective skill training scheme and an objective skill level assessment, we analyzed metal filing and implemented a method to evaluate metal-filing skill. We used wearable hybrid sensors that support an accelerometer and gyroscope, and collected data from 4 expert coaches and 10 learners. The data are analyzed from the viewpoint of the mechanical structure of their bodies during metal filing. Our analysis yielded three effective measures for skill assessment: "Class 2 Lever-like Movement Measure", "Upper Body Rigidity Measure", and "Pre-Acceleration Measure".
The weighted total measure succeeded in distinguishing the coach group and the learner group as individual skill level groups at a 95% confidence level. The highest-level learner, the lowest-level learner, and the group of other learners were also able to be distinguished as individual skill level groups at a 95% confidence level; this is the same result as an expert coach's subjective score.

References

[1]
size-JPN 2004-2006. http://www.meti.go.jp/press/20071001007/20071001007.html, Oct. 7 2007.
[2]
A. Ahmadi, D. Rowlands, and D. James. Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve. Sports Technology, 2(3-4):129--136, 2009.
[3]
M. Bächlin and G. Tröster. Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive and Mobile Computing, 2011.
[4]
H. Ghasemzadeh and R. Jafari. Coordination analysis of human movements with body sensor networks: A signal processing model to evaluate baseball swings. 11(3):603--610, 2011.
[5]
H. Ghasemzadeh, V. Loseu, and R. Jafari. Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf. Journal of Ambient Intelligence and Smart Environments, 1(2):173--184, 2009.
[6]
H. Ghasemzadeh, V. Loseu, and R. Jafari. Structural action recognition in body sensor networks: Distributed classification based on string matching. Information Technology in Biomedicine, IEEE Transactions on, 14(2):425--435, 2010.
[7]
H. Ghassemzadeh, E. Guenterberg, S. Ostadabbas, and R. Jafari. A motion sequence fusion technique based on pca for activity analysis in body sensor networks. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 3146--3149. IEEE, 2009.
[8]
J. Harding, C. Mackintosh, D. Martin, A. Hahn, and D. James. Automated scoring for elite half-pipe snowboard competition: important sporting development or techno distraction? Sports Technology, 1(6):277--290, 2008.
[9]
J. Harding, J. Small, and D. James. Feature extraction of performance variables in elite half-pipe snowboarding using body mounted inertial sensors. In Proc. SPIE, volume 6799, page 679917, 2007.
[10]
J. Harding, K. Toohey, D. Martin, A. Hahn, and D. James. Technology and half-pipe snowboard competition --- insight from elite-level judges (p240). The Engineering of Sport 7, pages 467--476, 2008.
[11]
E. Heinz, K. Kunze, M. Gruber, D. Bannach, and P. Lukowicz. Using wearable sensors for real-time recognition tasks in games of martial arts-an initial experiment. In Computational Intelligence and Games, 2006 IEEE Symposium on, pages 98--102. IEEE, 2006.
[12]
R. Jafari, R. Bajcsy, S. Glaser, B. Gnade, M. Sgroi, and S. Sastry. Platform design for health-care monitoring applications. In High Confidence Medical Devices, Software, and Systems and Medical Device Plug-and-Play Interoperability, 2007. HCMDSS-MDPnP. Joint Workshop on, pages 88--94. IEEE, 2007.
[13]
K. Kojima, K. Mase, S. Tokai, T. Kawamoto, and T. Fujii. On-body multi-sensor analysis of metal filing performance for manufacturing skill training. IEEE International Symposium on Wearable Computing (ISWC2009), Advances in Wearable Computing 2009, pages 21--28, 2009.
[14]
T. Komura, A. Kuroda, and Y. Shinagawa. Nicemeetvr: facing professional baseball pitchers in the virtual batting cage. In Proceedings of the 2002 ACM symposium on Applied computing, pages 1060--1065. ACM, 2002.
[15]
M. Lapinski, E. Berkson, T. Gill, M. Reinold, and J. Paradiso. A distributed wearable, wireless sensor system for evaluating professional baseball pitchers and batters. In Wearable Computers, 2009. ISWC'09. International Symposium on, pages 131--138. IEEE, 2009.
[16]
A. Murata. Shoulder joint movement of the non-throwing arm during baseball pitch--comparison between skilled and unskilled pitchers. Journal of Biomechanics, 34(12):1643--1647, 2001.
[17]
M. Rizzo and M. Rizzo. Softball/baseball training machine, Oct. 23 2001. US Patent 6,305,366.
[18]
M. Robinson, L. Holt, T. Pelham, and K. Furneaux. Accelerometry measurements of sprint kayaks: The coaches' new tool. The Coach Education Internship Experience: An Exploratory Study/3 Kristen D. Dieffenbach West Virginia University, USA Melissa Murray The University of Southern Mississippi, USA, page 45, 2011.
[19]
D. Spelmezan, A. Schanowski, and J. Borchers. Wearable automatic feedback devices for physical activities. In Proceedings of the Fourth International Conference on Body Area Networks, page 1. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2009.
[20]
G. Straban. Golf swing training device, Nov. 7 1967. US Patent 3,351,346.
[21]
C. Theobalt, I. Albrecht, J. Haber, M. Magnor, and H. Seidel. Pitching a baseball: tracking high-speed motion with multi-exposure images. In ACM Transactions on Graphics (TOG), volume 23, pages 540--547. ACM, 2004.
[22]
F. Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80--83, 1945.
[23]
F. Wilcoxon. Probability tables for individual comparisons by ranking methods. Biometrics, 3(3):119--122, 1947.
[24]
Y. Yamamoto. An alternative approach to the acquisition of a complex motor skill: Multiple movement training on tennis strokes. International Journal of Sport and Health Science, 2(0):169--179, 2004.

Cited By

View all
  • (2019)Instrumenting and Analyzing Fabrication Activities, Users, and ExpertiseProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300554(1-14)Online publication date: 2-May-2019
  • (2015)Skill grouping methodProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7364049(2525-2534)Online publication date: 29-Oct-2015
  • (2013)The mobile fitness coachPervasive and Mobile Computing10.1016/j.pmcj.2012.06.0029:2(203-215)Online publication date: 1-Apr-2013

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AH '12: Proceedings of the 3rd Augmented Human International Conference
March 2012
162 pages
ISBN:9781450310772
DOI:10.1145/2160125
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]

Sponsors

  • Megeve: Megève Tourisme
  • University of Genova: University of Genova

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2012

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

AH '12
Sponsor:
  • Megeve
  • University of Genova
AH '12: Augmented Human International Conference
March 8 - 9, 2012
Megève, France

Acceptance Rates

Overall Acceptance Rate 121 of 306 submissions, 40%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Instrumenting and Analyzing Fabrication Activities, Users, and ExpertiseProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300554(1-14)Online publication date: 2-May-2019
  • (2015)Skill grouping methodProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7364049(2525-2534)Online publication date: 29-Oct-2015
  • (2013)The mobile fitness coachPervasive and Mobile Computing10.1016/j.pmcj.2012.06.0029:2(203-215)Online publication date: 1-Apr-2013

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