Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface

Abstract: With the development of artificial intelligence and Internet of Things, hand gesture recognition techniques have attracted great attention owing to their excellent applications in developing human‐machine interaction (HMI). Here, the authors propose a non‐contact hand gesture recognition method based on intelligent metasurface. Owing to the advantage of dynamically controlling the electromagnetic (EM) focusing in the wavefront engineering, a transmissive programmable metasurface is presented to illuminate the forearm with more focusing spots and obtain comprehensive echo data, which can be processed under the machine learning technology to reach the non‐contact gesture recognition with high accuracy. Compared with the traditional passive antennas, unique variations of echo coefficients resulted from near fields perturbed by finger and wrist agonist muscles can be aquired through the programmable metasurface by switching the positions of EM focusing. The authors realize the gesture recognition using support vector machine algorithm based on five individual focusing spots data and all‐five‐spot data. The influences of the focusing spots on the gesture recognition are analyzed through linear discriminant analysis algorithm and Fisher score. Experimental verifications prove that the proposed metasurface‐based non‐contact wireless design can realize the classification of hand gesture recognition with higher accuracy than traditional passive antennas, and give an HMI solution.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Noncontact Electromagnetic Wireless Recognition for Prosthesis Based on Intelligent Metasurface ; day:07 ; month:05 ; year:2022 ; extent:13 ; Advanced science ; (07.05.2022) (gesamt 13)

Beteiligte, Orts- und Zeitangaben

• Urheber: Wang, Hai Peng ; Zhou, Yu Xuan ; Li, He ; Liu, Guo Dong ; Yin, Si Meng ; Li, Peng Ju ; Dong, Shu Yue ; Gong, Chao Yue ; Wang, Shi Yu ; Li, Yun Bo ; Cui, Tie Jun

Weitere Informationen

• DOI: 10.1002/advs.202105056
• URN: urn:nbn:de:101:1-2022050715082535796677
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:23 MESZ