research-article

Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling

Authors:

Gerhard TrösterAuthors Info & Claims

IEEE Transactions on Information Technology in Biomedicine, Volume 14, Issue 6

Pages 1436 - 1445

https://doi.org/10.1109/TITB.2010.2076822

Published: 01 November 2010 Publication History

Abstract

A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs.We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis.

References

[1]

H. Zheng, N. Black, and N. Harris, "Position-sensing technologies for movement analysis in stroke rehabilitation," Med. Biol. Eng. Comput., vol. 43, no. 4, pp. 413-420, 2005.

[2]

M. Sung, C. Marci, and A. Pentland, "Wearable feedback systems for rehabilitation," J. Neuroeng. Rehabil., vol. 2, no. 1, p. 17, 2005.

[3]

H. Harms, O. Amft, D. Roggen, and G. Tröster, "Rapid prototyping of smart garments for activity-aware applications," J. Ambient Intell. Smart Environ., vol. 1, no. 2, pp. 87-101, 2009.

Digital Library

[4]

R. Paradiso, C. Belloc, G. Loriga, and N. Taccini, "Wearable healthcare systems, new frontiers of e-textile." in Personalised Health Management Systems: The Integration of Innovative Sensing, Textile, Information and Communication Technologies (Studies in Health Technology and Informatics series), vol. 117, Amsterdam, The Netherlands: IOS Press, 2005, pp. 9-16.

[5]

A. Lymberis and S. Olsson, "Intelligent biomedical clothing for personal health and disease management: State of the art and future vision," Telemed. J. E-Health, vol. 9, no. 4, pp. 379-386, 2003.

[6]

S. Coyle, D. Morris, K.-T. Lau, D. Diamond, and N. Moyna, "Textile-based wearable sensors for assisting sports performance," in Proc. 6th Int. Workshop Wearable and Implantable Body Sensor Netw., Washington, DC, 2009, pp. 307-311.

[7]

W. Wong and M. Wong, "Detecting spinal posture change in sitting positions with tri-axial accelerometers," Gait Posture, vol. 27, no. 1, pp. 168- 171, 2008.

[8]

H. Harms, O. Amft, M. Appert, R. Muller, and G. Tröster, "Wearable therapist: Sensing garments for supporting children improve posture," in Proc. 11th Int. Conf. Ubiquitous Compu., New York, 2009, pp. 85-88.

[9]

H. Harms, O. Amft, and G. Tröster, "Influence of a loose-fitting sensing garment on posture recognition in rehabilitation," in Proc. Biomed. Circuits and Syst. Conf., Piscataway, NJ, Nov. 2008, pp. 353-356.

[10]

A. Tognetti, F. Lorussi, R. Bartalesi, S. Quaglini, M. Tesconi, G. Zupone, and D. De Rossi, "Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation," J. Neuroeng. Rehabil., vol. 2, no. 1, p. 8, Mar. 2005.

[11]

C. Mattmann, O. Amft, H. Harms, C. Tröster, and F. Clemens, "Recognizing upper body postures using textile strain sensors," in Proc. 11th IEEE Int. Symp. Wearable Comput., Piscataway, NJ, Oct. 2007, pp. 29-36.

[12]

O. Amft, H. Harms, G. Troester, and C. Schuster, "Estimating exercise execution quality from garment-integrated sensors," Neurorehabil. Neural Repair, vol. 22, no. 5, p. 616, 2008.

[13]

K. Kunze and P. Lukowicz, "Dealing with sensor displacement in motion-based onbody activity recognition systems," in Proc. 10th Int. Conf. Ubiquitous comput., New York, 2008, pp. 20-29.

Digital Library

[14]

P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Tröster, "Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness," in Proc. 3rd Int. Conf. Intell. Sens., Sens. Netw. Inf., 2007 (ISSNIP 2007), Dec., pp. 281-286.

[15]

D. E. Breen, D. H. House, and J. M. Wozny, "A particle-based model for simulating the draping behavior of woven cloth," Textile Res. J., vol. 64, no. 11, pp. 663-685, 1992.

[16]

P. Volino and N. Magnenat-Thalmann, "Accurate garment prototyping and simulation," Comput.-Aided Design Appl., vol. 2, no. 1-4, pp. 645-654, 2005.

[17]

J. Hu and Y. Chan, "Effect of fabric mechanical properties on drape," Textile Res. J., vol. 68, no. 1, pp. 57-64, 1998.

[18]

D. Lojen and S. Jevsnik, "Some aspects of fabric drape," Fibres Textiles Eastern Eur., vol. 15, no. 4, pp. 39-45, Dec. 2007.

[19]

H. Harms, O. Amft, and G. Tröster, "Modeling and simulation of sensor orientation errors in garments," presented at the 4th Int. Conf. Body Area Netw., Los Angeles, CA, 2009.

[20]

T. Barker, C. Kirtley, and J. Ratanapinunchai, "Calculation of multisegment rigid body joint dynamics using matlab," in Proc. Inst. Mech. Eng., Part H, J. Eng. Med., 01 1997, pp. 483-487.

[21]

J. Denavit and R. S. Hartenberg, "A kinematic notation for lower-pair mechanisms based on matrices," Trans. ASME J. Appl. Mech, vol. 23, pp. 215-221, 1955.

[22]

A. Tilley, The Measure of Man and Woman : Human Factors in Design, 1st ed. New York: Whitney Library of Design, 1993.

[23]

mysmithmicro. (2010). "Poser 6," {Online}. Available: http://my.smithmicro.com/win/poser/index.html.

[24]

X. Dai, T. Furukawa, S. Mitsui, M. Takatera, and Y. Shimizu, "Drape formation based on geometric constraints and its application to skirt modelling," Sci. Technol., vol. 13, no. 1, pp. 23-37, 2001.

[25]

A. Bowman and A. Azzalini, Applied Smoothing Techniques for Data Analysis: The Kernel Approach With S-Plus Illustrations. New York: Oxford Univ. Press, 1997.

Cited By

Lin QPeng SWu YLiu JJia HHu WHassan MSeneviratne AWang C(2023)Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity RecognitionACM Transactions on Sensor Networks10.1145/358498619:4(1-23)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3584986
Gioberto GCoughlin JBibeau KDunne LVan Laerhoven KRoggen DGatica-Perez DFukumoto M(2013)Detecting bends and fabric folds using stitched sensorsProceedings of the 2013 International Symposium on Wearable Computers10.1145/2493988.2494355(53-56)Online publication date: 8-Sep-2013
https://dl.acm.org/doi/10.1145/2493988.2494355

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Published In

cover image IEEE Transactions on Information Technology in Biomedicine

IEEE Transactions on Information Technology in Biomedicine Volume 14, Issue 6

November 2010

179 pages

ISSN:1089-7771

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Copyright © 2010.

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IEEE Press

Publication History

Published: 01 November 2010

Accepted: 22 August 2010

Revised: 22 August 2010

Received: 30 June 2010

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Cited By

Lin QPeng SWu YLiu JJia HHu WHassan MSeneviratne AWang C(2023)Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity RecognitionACM Transactions on Sensor Networks10.1145/358498619:4(1-23)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3584986
Gioberto GCoughlin JBibeau KDunne LVan Laerhoven KRoggen DGatica-Perez DFukumoto M(2013)Detecting bends and fabric folds using stitched sensorsProceedings of the 2013 International Symposium on Wearable Computers10.1145/2493988.2494355(53-56)Online publication date: 8-Sep-2013
https://dl.acm.org/doi/10.1145/2493988.2494355

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