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Human augmentation hand for cooperative solving of dissection puzzle problem

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Abstract

Human augmentation technologies can strengthen and compensate for the lack of human abilities associated with aging and physical disabilities. Efforts have been made to apply these technologies in manufacturing and agricultural industries to improve work efficiency, reduce fatigue, and cover differences in workers’ skills. This study proposes a human augmentation hand to assist users in performing intelligent tasks requiring high brain functions, such as cognition, planning, judgment, and memory. Solving a dissection puzzle is used as an example of an intelligent task. The human augmentation hand has a view of the puzzle blocks with an attached camera, and the puzzle blocks required for solving the dissection puzzle are derived using a full-search algorithm. The system can then provide user hints for solving puzzles and assist in handling puzzle blocks. An experiment is conducted to confirm the operation of the system and examine its usefulness. A National Aeronautics and Space Administration task load index (NASA-TLX) questionnaire is used to investigate the subjective workload perceived by users. The experimental results reveal that the proposed system improves task efficiency and reduces workloads that require high brain functions.

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References

  1. Stöckel T, Wunsch K, Hughes CML (2017) Age-related decline in anticipatory motor planning and its relation to cognitive and motor skill proficiency. Front Aging Neurosci 9:283

    Article  Google Scholar 

  2. Krampe RT (2002) Aging, expertise and fine motor movement. Neurosci Biobehav Rev 26(7):769–776

    Article  Google Scholar 

  3. Knobl P, Kielstra L, Almeida Q (2011) The relationship between motor planning and freezing of gait in Parkinson’s disease. J Neurol Neurosurg Psychiatry 83(1):98–101

    Article  Google Scholar 

  4. Barhoun P, Fuelscher I, Kothe EJ, He JL, Youssef GJ, Enticott PG, Williams J, Hyde C (2019) Motor imagery in children with DCD: a systematic and meta-analytic review of hand-rotation task performance. Neurosci Biobehav Rev 99:282–297

    Article  Google Scholar 

  5. Vimercati SL, Galli M, Stella G, Caiazzo G, Ancillao A, Albertini G (2014) Clumsiness in fine motor tasks: evidence from the quantitative drawing evaluation of children with Down Syndrome. J Intellect Disabil Res 59(3):248–256

    Article  Google Scholar 

  6. Ito S, Kawasaki H, Ishigure Y, Natsume M, Mouri T, Nishimoto Y (2011) A design of fine motion assist equipment for disabled hand in robotic rehabilitation system. J Franklin Inst 348(1):79–89

    Article  Google Scholar 

  7. Sankai Y (2006) Leading edge of cybernics: robot suit HAL. In: 2006 SICE-ICASE International Joint Conference, pp 1–2

  8. Rekimoto J (2021) The prospect for human augmentation technologies. J Inf Stud Interfaculty Initiat Inf Stud Univ Tokyo 100:19–45

    Google Scholar 

  9. Saraiji MHDY, Sasaki T, Matsumura R, Minamizawa K, Inami M (2018) Fusion: full body surrogacy for collaborative communication. In: SIGGRAPH Emerging Technologies, Article No. 7, pp. 1–2

  10. Kato H, Kobayashi T, Tsuji T, Sugano M, Yanagihara H (2017) Research and development of a remote field support system using augmented reality for smart glasses. J Inst Image Inf Telev Eng 71:J35–J43

    Google Scholar 

  11. He Y, Kubozono R, Fukuda O, Yamaguchi N, Okumura H (2020) Vision-based assistance for myoelectric hand control. IEEE Access 8:201956–201965

    Article  Google Scholar 

  12. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788

  13. Bin X, Salvendy G (2000) Prediction of mental workload in single and multiple tasks environments. Int J Cogn Ergon 4(3):213–242

    Article  Google Scholar 

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Correspondence to Osamu Fukuda.

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This work was presented in part at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 25-27, 2023).

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Yoshida, K., Yeoh, W.L., Okumura, H. et al. Human augmentation hand for cooperative solving of dissection puzzle problem. Artif Life Robotics 29, 120–128 (2024). https://doi.org/10.1007/s10015-023-00922-7

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  • DOI: https://doi.org/10.1007/s10015-023-00922-7

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