A skin-like sensory system, consisting of a substrate-less nanomesh strain sensor and an unsupervised meta-learning framework, enables the rapid recognition of various hand movements with minimal training and can work for any user. The device is able to complete various tasks, including virtual keyboard typing and object recognition.
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References
Kim, K. K. et al. Smart stretchable electronics for advanced human–machine interface. Adv. Intell. Syst. 3, 200157 (2020). A review article that presents cutting-edge human–machine interface devices.
Moin, A. et al. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition. Nat. Electron. 4, 54–63 (2021). This paper reports a wearable surface electromyography measurement system combined with machine intelligence to recognize various hand gestures.
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Kim, K. K. et al. A deep-learned skin sensor decoding the epicentral human motions. Nat. Commun 11, 2149 (2020). This paper demonstrates the use of machine intelligence combined with a wearable sensor to decode multi-hand gestures with a single skin sensor.
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This is a summary of: Kim, K. K. et al. A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition. Nat. Electron. https://doi.org/10.1038/s41928-022-00888-7 (2022).
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A skin sensor that can rapidly recognize hand-based tasks with limited training. Nat Electron 6, 8–9 (2023). https://doi.org/10.1038/s41928-022-00889-6
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DOI: https://doi.org/10.1038/s41928-022-00889-6
