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Master-Slave Control of Soft Robotic Glove Based on Carbon Black Strain Sensing

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 15207))

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Abstract

Despite the effectiveness of soft robotic gloves in providing rehabilitation training and grip assistance for patients with hand injuries, their actuators’ lack of integrated sensors hampers precise position control, posing challenges for individuals with heightened muscular tension who are unable to achieve the desired bending angle due to this absence of feedback. In this paper, we propose a master-slave rehabilitation scheme for controlling a robotic glove using a data glove. The carbon black-based flexible strain sensor seamlessly integrates with the data glove and actuators, making it wearable and portable. The strain sensor's response was characterized in terms of strain amplitude, dynamic stability, strain rate, and fatigue characteristics. It demonstrates exceptional stretchability up to 100% with a gauge factor ranging from 1.14 to 1.34. The finger movement and grasping response were evaluated using a data glove, revealing robust capability for generating reliable control signals. The sensors were calibrated to achieve precise control of the actuator using the data glove, and a quantification was made of the relationship between finger bending angle and sensor response. A comparison was conducted between the bending angle that can be achieved by the actuator with or without feedback through establishing a pneumatic control system. The results indicate that with position feedback, maximum angle errors for thumb and index finger are 5.9° and 7.5° respectively. Furthermore, gesture recognition master-slave control experiments were carried out to provide additional evaluation on the efficacy of our proposed hand rehabilitation system.

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Acknowledgement

The authors would like to gratefully acknowledge the reviewers comments. This work was supported in part by the National Natural Science Foundation of China Regional Innovation and Development Joint Fund (Anhui) under Grant U23A20338, in part by the National Natural Science Foun-dation of China under Grant 62203044, and in part by Beijing Natural Science Foundation under Grant 3232020.

Author information

Authors and Affiliations

  1. Hebei University of Technology, Tianjin, China

    Xiangli Li & Hui Wang

  2. University of Science and Technology Beijing, Beijing, China

    Yufei Hao & Jianhua Zhang

Authors
  1. Xiangli Li

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  2. Yufei Hao

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  3. Hui Wang

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  4. Jianhua Zhang

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Corresponding author

Correspondence toYufei Hao.

Editor information

Editors and Affiliations

  1. Xi’an Jiaotong University, Xi’an, China

    Xuguang Lan

  2. Xi’an Jiaotong University, Xi’an, China

    Xuesong Mei

  3. Xi’an Jiaotong University, Xi’an, China

    Caigui Jiang

  4. Xi’an Jiaotong University, Xi’an, China

    Fei Zhao

  5. Xi'an Jiaotong University, Xi'an, China

    Zhiqiang Tian

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© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, X., Hao, Y., Wang, H., Zhang, J. (2025). Master-Slave Control of Soft Robotic Glove Based on Carbon Black Strain Sensing. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15207. Springer, Singapore. https://doi.org/10.1007/978-981-96-0780-8_16

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