Enhancing prosthetic control through high-fidelity myoelectric mapping with molecular anchoring technology

Myoelectric control utilizes electrical signals generated from the voluntary contraction of remaining muscles in an amputee's stump to operate a prosthesis. Precise and agile control requires low-level myoelectric signals (below 10% of maximum voluntary contraction, MVC) from weak muscle contra...

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Bibliographic Details
Main Authors: Pan, Liang, Wang, Hui, Huang, Pingao, Wu, Xuwei, Tang, Zihan, Jiang, Ying, Ji, Shaobo, Cao, Jinwei, Ji, Baohua, Li, Guanglin, Li, Dechang, Wang, Zhiming, Chen, Xiaodong
Other Authors: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/169303
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Institution: Nanyang Technological University
Language: English
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Summary:Myoelectric control utilizes electrical signals generated from the voluntary contraction of remaining muscles in an amputee's stump to operate a prosthesis. Precise and agile control requires low-level myoelectric signals (below 10% of maximum voluntary contraction, MVC) from weak muscle contractions such as phantom finger or wrist movements, but imbalanced calcium concentration in atrophic skin can distort the signals. This is due to poor ionic-electronic coupling between skin and electrode, which often causes excessive muscle contraction, fatigue, and discomfort during delicate tasks. To overcome this challenge, a new strategy called molecular anchoring is developed to drive hydrophobic molecular effectively interact with and embed into stratum corneum for high coupling regions between ionic fluxes and electronic currents. The use of hydrophobic poly(N-vinyl caprolactam) gel has resulted in an interface impedance of 20 kΩ, which is 1/100 of a commercial acrylic-based electrode, allowing the detection of ultralow myoelectric signals (≈1.5% MVC) that approach human limits. With this molecular anchoring technology, amputees operate a prosthesis with greater dexterity, as phantom finger and wrist movements are predicted with 97.6% accuracy. This strategy provides the potential for a comfortable human-machine interface when amputees accomplish day-to-day tasks through precise and dexterous myoelectric control.