Flexible wide-range multidimensional force sensors inspired by bones embedded in muscle

Flexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection....

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Veröffentlicht in:Microsystems & nanoengineering 2024-05, Vol.10 (1), p.64-64, Article 64
Hauptverfasser: Zhang, Jie, Hou, Xiaojuan, Qian, Shuo, Huo, Jiabing, Yuan, Mengjiao, Duan, Zhigang, Song, Xiaoguang, Wu, Hui, Shi, Shuzheng, Geng, Wenping, Mu, Jiliang, He, Jian, Chou, Xiujian
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Sprache:eng
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Zusammenfassung:Flexible sensors have been widely studied for use in motion monitoring, human‒machine interactions (HMIs), personalized medicine, and soft intelligent robots. However, their practical application is limited by their low output performance, narrow measuring range, and unidirectional force detection. Here, to achieve flexibility and high performance simultaneously, we developed a flexible wide-range multidimensional force sensor (FWMFS) similar to bones embedded in muscle structures. The adjustable magnetic field endows the FWMFS with multidimensional perception for detecting forces in different directions. The multilayer stacked coils significantly improved the output from the μV to the mV level while ensuring FWMFS miniaturization. The optimized FWMFS exhibited a high voltage sensitivity of 0.227 mV/N (0.5–8.4 N) and 0.047 mV/N (8.4–60 N) in response to normal forces ranging from 0.5 N to 60 N and could detect lateral forces ranging from 0.2–1.1 N and voltage sensitivities of 1.039 mV/N (0.2–0.5 N) and 0.194 mV/N (0.5–1.1 N). In terms of normal force measurements, the FWMFS can monitor finger pressure and sliding trajectories in response to finger taps, as well as measure plantar pressure for assessing human movement. The plantar pressure signals of five human movements collected by the FWMFS were analyzed using the k-nearest neighbors classification algorithm, which achieved a recognition accuracy of 92%. Additionally, an artificial intelligence biometric authentication system is being developed that classifies and recognizes user passwords. Based on the lateral force measurement ability of the FWMFS, the direction of ball movement can be distinguished, and communication systems such as Morse Code can be expanded. This research has significant potential in intelligent sensing and personalized spatial recognition.
ISSN:2055-7434
2096-1030
2055-7434
DOI:10.1038/s41378-024-00711-7