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A new IMMU-based data glove for hand motion capture with optimized sensor layout

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Abstract

The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient’s hand ability in home environment. While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 51675389 and 51705381, partially by Nature Science Foundation of Hubei Province (2017CFB428) and Overseas S&T Cooperation, and Fundamental Research Funds for the Central Universities (WUT: 2018IVB081, 2018IVA100).

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Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Quan Liu, Guoming Qian, Wei Meng, Qingsong Ai & Chaoyue Yin

  2. School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK

    Wei Meng

  3. Department of Rehabilitation Medicine, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, 430030, China

    Zhengyu Fang

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  1. Quan Liu
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  2. Guoming Qian
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  3. Wei Meng
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  4. Qingsong Ai
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  5. Chaoyue Yin
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  6. Zhengyu Fang
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Correspondence to Qingsong Ai.

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Liu, Q., Qian, G., Meng, W. et al. A new IMMU-based data glove for hand motion capture with optimized sensor layout. Int J Intell Robot Appl 3, 19–32 (2019). https://doi.org/10.1007/s41315-019-00085-4

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  • DOI: https://doi.org/10.1007/s41315-019-00085-4

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