Abstract
Predicting human stiffness, especially at the distal end of the human arm, holds significant potential for various applications. It facilitates the realization of humanoid stiffness regulation in robots, improves the adaptability and human-likeness of interactive robots, and addresses critical issues in human control of medical assistive robots. Recognizing that surface electromyographic (EMG) signals not only contain rich information but are also easy to collect and process, they serve as an optimal choice for predicting human stiffness. To establish a mapping relationship between surface EMG signals and stiffness information, we constructed a stiffness acquisition system to collect signals such as EMG, angular, force, and displacement signals. Additionally, considering the influence of different angles (configurations) of the human arm on the stiffness at the distal end, we researched a stiffness prediction model for the distal end of the human arm using a multilayer perceptron. Experimental results demonstrate that our proposed stiffness prediction model, utilizing EMG information provided by the EMG armband along with angular information, can predict the stiffness at the distal end of the human arm in various scenarios. This provides ample reference for achieving humanoid stiffness regulation in medical assistive robots.
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The data presented in this study are available on request from the corresponding author.
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Funding
This research was supported by Self-Planned Task (NO. SKLRS202207B) of State Key Laboratory of Robotics and System (HIT) and National Key Research and Development Program of China (Grant No. 2019YFB1311303). This research was funded by National Key Research and Development Program of China (Grant No. 2019YFB1311303), Natural Science Foundation of China (Grant No. U1713202), and Major scientific and technological innovation projects in Shandong Province (Grant No. 2019JZZY010430).
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Chong Yao: Conceptualization, Methodology, Software Data curation, Writing – original draft preparation, Changle Li: Conceptualization, Methodology, Funding acquisition, Yihan Shan: Methodology, Xuehe Zhang: Conceptualization, Visualization, Leifeng Zhang: Methodology, Software Data curation, Jie Zhao: Supervision, Funding acquisition.
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Yao, C., Li, C., Shan, Y. et al. Research on stiffness adaptation control of medical assistive robots based on stiffness prediction. Int J Intell Robot Appl 8, 503–516 (2024). https://doi.org/10.1007/s41315-024-00321-6
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DOI: https://doi.org/10.1007/s41315-024-00321-6