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Grasp Synthesis for the Hands of Elderly People with Reduced Muscular Force, Slippery Skin, and Limitation in Range of Motion

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Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication (HCII 2022)

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Abstract

In this paper, we propose a synthesis method for grasping postures by using a digital hand for the elderly. For the virtual evaluation of inclusive design products, it is necessary to synthesize natural grasps for non-healthy hands, including those of the elderly. In modeling the hand of the elderly, we focused on the following three features: (1) narrowed range of motion (ROM), (2) decline in muscle strength, and (3) decline in friction coefficient. The modeling was based on existing studies of physical characteristics of the elderly, and it is possible to create hand models of any age from 20 to 100 years old. We improved our previous method for synthesizing grasps, which was developed for hands with limited thumb ROM, and synthesized grasping postures for elderly people. We found that the grasping posture of the 60-year-old hand was slightly different from that of the healthy hand and that the 100-year-old hand experienced great difficulty in grasping objects.

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References

  1. Coleman, R., Lebbon, C., Clarkson, J., Keates, S.: From margins to mainstream. In: Clarkson, J., Keates, S., Coleman, R., Lebbon, C. (eds.) Inclusive Design: Design for the Whole Population, pp. 10–12. Springer, London (2003). https://doi.org/10.1007/978-1-4471-0001-0

  2. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis-a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)

    Article  Google Scholar 

  3. Kyota, F., Watabe, T., Saito, S., Nakajima, M.: Detection and evaluation of grasping positions for autonomous agents. In: Proceedings of 2005 International Conference on Cyberworlds, pp. 453–460. IEEE, Singapore (2005)

    Google Scholar 

  4. Ciocarlie, M.T., Allen, P.K.: Hand posture subspaces for dexterous robotic grasping. Int. J. Robot. Res. 28(7), 851–867 (2009)

    Article  Google Scholar 

  5. Goldfeder, C., Allen, P.K.: Data-driven grasping. Auton. Robots 31(1), 1–20 (2011)

    Article  Google Scholar 

  6. Miyata, N., Hirono, T., Maeda, Y.: Grasp database that covers variation of contact region usage. In: Proceedings of 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2635–2040. IEEE, UK (2013)

    Google Scholar 

  7. Hirono, T., Miyata, N., Maeda, Y.: Grasp synthesis for variously-sized hands using a grasp database that covers variation of contact region. In: Proceedings of the 3rd International Digital Human Modeling Symposium, #11, Japan (2014)

    Google Scholar 

  8. Feix, T., Pawlik, R., Schmiedmayer, H-B., Romero, J., Kragic, D.: A comprehensive grasp taxonomy. In: Robotics, Science and Systems Conference: Workshop on Understand the Human Hand for Advanced Robotic Manipulation, Switzerland (2009)

    Google Scholar 

  9. Feix, T., Romero, J., Schmiedmayer, H.-B., Dollar, A.M., Kragic, D.: The GRASP taxonomy of human grasp types. IEEE Trans. Hum.-Mach. Syst. 46(1), 66–77 (2016)

    Article  Google Scholar 

  10. Napier, J.R.: The prehensile movements of the human hand. J. Bone Joint Surg. 38(4), 902–913 (1956)

    Article  Google Scholar 

  11. Kamakura, N., Ohmura, M., Ishii, H., Mitsuboshi, F., Miura, Y.: Positional patterns for prehension in normal hands. J. Rehabil. Med. 15(2), 65–82 (1978). (in Japanese)

    Google Scholar 

  12. Lee, K.-S., Jung, M.-C.: Common patterns of voluntary grasp types according to object shape, size, and direction. Int. J. Ind. Ergon. 44, 761–68 (2014)

    Article  Google Scholar 

  13. Takahashi, R., Miyata, N., Maeda, Y., Nakanishi, Y.: Grasp synthesis considering graspability for a digital hand with limited thumb range of motion. Adv. Robot. 36(4), 192–204 (2022)

    Google Scholar 

  14. Asami, Y., Tada, M., Endo, Y., Ogiwara, N.: Estimation of grasping posture by minimizing internal mechanical load during power grip task using a three-dimensional musculoskeletal model of the human hand. In: Proceedings of the 2017 JSME Conference on Robotics and Mechatronics, 2P2-J01, Japan (2017). (in Japanese)

    Google Scholar 

  15. Doriot, N., Wang, X.: Effects of age and gender on maximum voluntary range of motion of the upper body joints. Ergonomics 49(3), 269–281 (2016)

    Article  Google Scholar 

  16. Vandervoort, A.A.: Aging of the human neuromascular system. Muscle Nerve 25(1), 17–25 (2002)

    Article  Google Scholar 

  17. Mabuchi, K., Sakai, R., Yoshida, K., Ujihira, M.: Effect of ageing on friction of human fingers. Biosurf. Biotribol. 4(4), 117–121 (2018)

    Article  Google Scholar 

  18. Potts, R.O., Buras, E.M., Jr., Chrisman, D.A., Jr.: Changes with age in the moisture content of human skin. J. Investig. Dermatol. 82(1), 97–100 (1984)

    Article  Google Scholar 

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Acknowledgement

This work was partly supported by JSPS KAKENHI Grant Numbers JP17H05918, JP20H04268, and JP20J20651.

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Authors and Affiliations

  1. Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama, Japan

    Reiko Takahashi, Yuta Nakanishi & Yusuke Maeda

  2. National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo, Japan

    Natsuki Miyata

Authors
  1. Reiko Takahashi
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  2. Yuta Nakanishi
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  3. Natsuki Miyata
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  4. Yusuke Maeda
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Corresponding author

Correspondence to Reiko Takahashi .

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Editors and Affiliations

  1. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

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Takahashi, R., Nakanishi, Y., Miyata, N., Maeda, Y. (2022). Grasp Synthesis for the Hands of Elderly People with Reduced Muscular Force, Slippery Skin, and Limitation in Range of Motion. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication. HCII 2022. Lecture Notes in Computer Science, vol 13319. Springer, Cham. https://doi.org/10.1007/978-3-031-05890-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-05890-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05889-9

  • Online ISBN: 978-3-031-05890-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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