The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Wearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application
Abstract
In comparison to traditional bulky and rigid electronic devices, the human–machine interaction (HMI) system with flexible and wearable components is an inevitable future trend. To achieve effective, intuitive, and seamless manipulation of high‐performance wearable HMI systems, it is important to develop effective strategies for designing material microstructures on flexible sensors and electric devices with excellent mechanical flexibility and stretchability. The real‐time acquisition of human physiology and surrounding signals through accurate and flexible sensors is the basis of wearable HMIs. Herein, the construction of a wearable HMI system that utilizes sensors, communication modes, and actuators is reviewed. The mechanisms and strategies for designing various flexible sensors based on different mechanisms are analyzed and discussed. The functional mechanism, material selection, and novel design strategies of each part are summarized in detail. The different communication modes in interactive systems and the manufacturing technology of soft machines are also introduced. Additionally, the most advanced applications of wearable HMI systems in intelligent identification and security, interactive controls for robots, augmented reality, and virtual reality have been highlighted. The review concludes with an overview of the remaining key challenges and several ideas regarding the further improvement of wearable HMI systems.
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Cites
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., et al. (2016) · Nature · doi:10.1038/nature16521 · Cited by 5,087
Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., et al. (2011) · Science · doi:10.1126/science.1206157 · Cited by 4,836
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
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
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719
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
A wearable and highly sensitive pressure sensor with ultrathin gold nanowires
Gong, S., Schwalb, W., Wang, Y., Chen, Y., Tang, Y., Si, J., Shirinzadeh, B., & Cheng, W. (2014) · Nature Communications · doi:10.1038/ncomms4132 · Cited by 2,064
The rise of plastic bioelectronics
Someya, T., Bao, Z., & Malliaras, G. G. (2016) · Nature · doi:10.1038/nature21004 · Cited by 1,884
Wang, Z. L. (2014) · Faraday Discuss. · doi:10.1039/c4fd00159a · Cited by 1,749
Cited by
Advances in graphene-based flexible and wearable strain sensors
Chen, H., Zhuo, F., Zhou, J., Liu, Y., Zhang, J., Dong, S., Liu, X., Elmarakbi, A., Duan, H., & Fu, Y. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.142576 · Cited by 386
Fiber/Yarn and Textile-Based Piezoresistive Pressure Sensors
Yang, Y., Liu, Y., & Yin, R. (2025) · Advanced Fiber Materials · doi:10.1007/s42765-024-00479-5 · Cited by 110
Self-citation
Metadata
- Authors
- Yin, R., Wang, D., Zhao, S., Lou, Z., & Shen, G.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2020-12-29
- Volume
- 31
- Issue
- 11
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W3117266371
- Cited by
- 769
- References
- 40
- FWCI
- 21.16
- Type
- journal article
- Language
- en
- Open access
- closed
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