The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A deep-learned skin sensor decoding the epicentral human motions
Open accessAbstract
State monitoring of the complex system needs a large number of sensors. Especially, studies in soft electronics aim to attain complete measurement of the body, mapping various stimulations like temperature, electrophysiological signals, and mechanical strains. However, conventional approach requires many sensor networks that cover the entire curvilinear surfaces of the target area. We introduce a new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network. The device detects minute deformations from the unique laser-induced crack structures. A single skin sensor decodes the complex motion of five finger motions in real-time, and the rapid situation learning (RSL) ensures stable operation regardless of its position on the wrist. The sensor is also capable of extracting gait motions from pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Fracture Mechanics: Fundamentals and Applications
Anderson, T. L. (2017) · CRC Press eBooks · doi:10.1201/9781315370293 · Cited by 4,737
Proske, U., & Gandevia, S. C. (2012) · Physiological Reviews · doi:10.1152/physrev.00048.2011 · Cited by 2,223
Fracture mechanics : fundamentals and applications
Anderson, T. (1995) · Cited by 2,002
Fracture Mechanics: Fundamentals and Applications, Third Edition
Anderson, T. L., & Anderson, T. L. (2005) · CRC Press eBooks · doi:10.1201/9781420058215 · Cited by 1,854
Ultrasensitive mechanical crack-based sensor inspired by the spider sensory system
Kang, D., Pikhitsa, P. V., Choi, Y. W., Lee, C., Shin, S. S., Piao, L., Park, B., Suh, K.-Y., Kim, T.-I., & Choi, M. (2014) · Nature · doi:10.1038/nature14002 · Cited by 1,626
Embedded 3D Printing of Strain Sensors within Highly Stretchable Elastomers
Muth, J. T., Vogt, D. M., Truby, R. L., Mengüç, Y., Kolesky, D. B., Wood, R. J., & Lewis, J. A. (2014) · Advanced Materials · doi:10.1002/adma.201400334 · Cited by 1,611
Torrey, L., & Shavlik, J. (2010) · Handbook of Research on Machine Learning Applications and Trends · doi:10.4018/978-1-60566-766-9.ch011 · Cited by 1,503
Learning the signatures of the human grasp using a scalable tactile glove
Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., & Matusik, W. (2019) · Nature · doi:10.1038/s41586-019-1234-z · Cited by 1,162
Fractal design concepts for stretchable electronics
Fan, J. A., Yeo, W.-H., Su, Y., Hattori, Y., Lee, W., Jung, S.-Y., Zhang, Y., Liu, Z., Cheng, H., Falgout, L., et al. (2014) · Nature Communications · doi:10.1038/ncomms4266 · Cited by 1,088
Cited by
Sun, Z., Zhu, M., Shan, X., & Lee, C. (2022) · Nature Communications · doi:10.1038/s41467-022-32745-8 · Cited by 532
Wen, F., Zhang, Z., He, T., & Lee, C. (2021) · Nature Communications · doi:10.1038/s41467-021-25637-w · Cited by 494
3D Printed Flexible Strain Sensors: From Printing to Devices and Signals
Liu, H., Zhang, H., Han, W., Lin, H., Li, R., Zhu, J., & Huang, W. (2021) · Advanced Materials · doi:10.1002/adma.202004782 · Cited by 441
Piezoelectric nanogenerators for personalized healthcare
Deng, W., Zhou, Y., Libanori, A., Chen, G., Yang, W., & Chen, J. (2022) · Chemical Society Reviews · doi:10.1039/d1cs00858g · Cited by 434
Sun, T., Feng, B., Huo, J., Xiao, Y., Wang, W., Peng, J., Li, Z., Du, C., Wang, W., Zou, G., et al. (2024) · Nano-Micro Letters · doi:10.1007/s40820-023-01235-x · Cited by 384
Metadata
- Authors
- Kim, K. K., Ha, I., Kim, M., Choi, J., Won, P., Jo, S., & Ko, S. H.
- Venue
- Nature Communications
- Published
- 2020-05-01
- Volume
- 11
- Issue
- 1
- Pages
- 2149-2149
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3023818716
- Cited by
- 299
- References
- 41
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- gold
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- Kim, K. K., Ha, I., Kim, M., Choi, J., Won, P., Jo, S., & Ko, S. H. (2020). A deep-learned skin sensor decoding the epicentral human motions. Nature Communications. https://doi.org/10.1038/s41467-020-16040-y
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
