The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems
Abstract
With the prospect of a smart society in the foreseeable future, humans are experiencing an increased link to electronics in the digital world, which can benefit our life and productivity drastically. In recent decades, advances in the Human Machine Interface (HMI) have improved from tactile sensors, such as touchpads and joysticks, to now include the accurate detection of dexterous body movements in more diversified and sophisticated devices. Advancements in highly adaptive machine learning techniques, neural interfaces, and neuromorphic sensing have generated the potential for an economic and feasible solution for next-generation applications such as wearable HMIs with intimate and multi-directional sensing capabilities. This review offers a general knowledge of HMI technologies beginning with tactile sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on how machine learning, neural interfaces, and neuromorphic electronics can be used to enhance next-generation HMIs in an upcoming 5 G infrastructure and advancements in the internet of things and artificial intelligence of things in the near future. The efficient interactions with kinetic and physiological signals from human body through the fusion of tactile sensor and neural electronics will bring a revolution to both the advanced manipulation and medical rehabilitation.
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Cites
Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers
Mannsfeld, S. C. B., Tee, B. C.-K., Stoltenberg, R. M., Chen, C. V. H.-H., Barman, S., Muir, B. V. O., Sokolov, A. N., Reese, C., & Bao, Z. (2010) · Nature Materials · doi:10.1038/nmat2834 · Cited by 3,291
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
An ultra-lightweight design for imperceptible plastic electronics
Kaltenbrunner, M., Sekitani, T., Reeder, J., Yokota, T., Kuribara, K., Tokuhara, T., Drack, M., Schwödiauer, R., Graz, I., Bauer-Gogonea, S., et al. (2013) · Nature · doi:10.1038/nature12314 · Cited by 2,485
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Highly Stretchable and Sensitive Strain Sensor Based on Silver Nanowire–Elastomer Nanocomposite
Amjadi, M., Pichitpajongkit, A., Lee, S., Ryu, S., & Park, I. (2014) · ACS Nano · doi:10.1021/nn501204t · Cited by 2,350
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092
A bioinspired flexible organic artificial afferent nerve
Kim, Y., Chortos, A., Xu, W., Liu, Y., Oh, J. Y., Son, D., Kang, J., Foudeh, A. M., Zhu, C., Lee, Y., et al. (2018) · Science · doi:10.1126/science.aao0098 · Cited by 1,466
Stretchable silicon nanoribbon electronics for skin prosthesis
Kim, J., Lee, M., Shim, H. J., Ghaffari, R., Cho, H. R., Son, D., Jung, Y. H., Soh, M., Choi, C., Jung, S., et al. (2014) · Nature Communications · doi:10.1038/ncomms6747 · Cited by 1,435
Cited by
Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications
Jin, T., Sun, Z., Li, L., Zhang, Q., Zhu, M., Zhang, Z., Yuan, G., Chen, T., Tian, Y., Hou, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19059-3 · Cited by 695
Self-citationSun, Z., Zhu, M., Shan, X., & Lee, C. (2022) · Nature Communications · doi:10.1038/s41467-022-32745-8 · Cited by 532
Self-citationWen, F., Zhang, Z., He, T., & Lee, C. (2021) · Nature Communications · doi:10.1038/s41467-021-25637-w · Cited by 494
Self-citationSharma, A., Ansari, M. Z., & Cho, C. (2022) · Sensors and Actuators A: Physical · doi:10.1016/j.sna.2022.113934 · Cited by 95
Metadata
- Authors
- Zhu, M., He, T., & Lee, C.
- Venue
- Applied Physics Reviews
- Publisher
- AIP Publishing
- Published
- 2020-08-31
- Volume
- 7
- Issue
- 3
- ISSN
- 1931-9401 (ISSN-L)
- OpenAlex
- W3082001659
- Cited by
- 287
- References
- 35
- FWCI
- 14.23
- Type
- journal article
- Language
- en
- Open access
- closed
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