Experts developed an e-skin that can decode complex human motions using enhanced deep-learning technology. According to Tech Xplore's latest report, a sensor that can act as an electronic skin was recently developed by the researchers at Korea Advanced Institute of Science and Technology (KAIST) and Seoul National University, integrating it with a deep neural network.
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The newly developed e-skin system can capture human dynamic motions, including rapid finger movements from a distance using enhanced deep-learning technology; the innovation was presented in a paper published in Nature Communications. Experts in the fields of computer science and mechanical engineering had an interdisciplinary collaboration that gave way for the development of the new system. Sungho Jo, a computing professor at KAIST, and Seung Hwan Ko, a professor of mechanical engineering at Soul National University, lead the new study.
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Professor Seung Hwan Ko has been generating cracks in metal nanoparticle films using laser technology over the past years in the hope of developing highly sensitive strain sensors. The researchers integrated the resulting sensor arrays to a virtual reality (VR) glove specifically designed to identify human movements.
How can skin sensors capture complex human motions using deep-learning technology?
According to Tech Xplore, researchers developed an electronic skin sensor that decoded complex human motions, such as rapid finger movements, using enhanced deep-learning technology. Professor Seung Hwan Ko said the complexity of the target system increases. The required number of strain sensors also increases, which is why his lab typically uses five to ten strain sensors to predict complex hand motion; each finger must be integrated with at least one or two sensors.
