The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Breath-based human–machine interaction system using triboelectric nanogenerator
Abstract
Abstract Human-machine interface (HMI) is an emerging technology to exchange information between humans and electric devices. In current HMI devices, their crucial sensing components are mainly based on the interaction of physical movements and languages. However, these conventional HMI interacting methods are not friendly to some disabled persons owing to the difficulty to clearly express their intentions with gestures, limb motions, or languages. Additionally, these HMI sensing parts rely on traditional batteries, which require frequent charging or replacing. Here, we report a breath-driven single-electrode triboelectric nanogenerator (TENG) serving as a self-powered sensor to deliver control command through breathing for the HMI interaction. The TENG is on the basis of a flexible nanowire-structured polyethylene terephthalate (PET) polymer thin film, which can harvest mechanical energy of the airflow from human breathing and produce responsive electrical signals. By using this TENG as a self-powered sensor together with signal processing and transmitting circuits, a smart wireless breath-driven HMI system is further developed. Compared with the conventional HMI interacting methods, this smart system can convert real-time human breathing into command signals to control electrical appliances without relying on physical movements or languages. This work introduces a self-powered breath-based interacting method to the field of HMI technology. It could greatly reduce the threshold of using modern electrical equipment and personal electronics for disabled people, and make the HMI interaction become more convenient and captivating.
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Cites
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364
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
Cited by
Liang, X., Jiang, T., Liu, G., Feng, Y., Zhang, C., & Wang, Z. L. (2020) · Energy & Environmental Science · doi:10.1039/c9ee03258d · Cited by 398
Li, S., Fan, Y., Chen, H., Nie, J., Liang, Y., Tao, X., Zhang, J., Chen, X., Fu, E., & Wang, Z. L. (2020) · Energy & Environmental Science · doi:10.1039/c9ee03307f · Cited by 356
Technology evolution from self-powered sensors to AIoT enabled smart homes
Dong, B., Shi, Q., Yang, Y., Wen, F., Zhang, Z., & Lee, C. (2021) · Nano Energy · doi:10.1016/j.nanoen.2020.105414 · Cited by 313
Triboelectric Touch‐Free Screen Sensor for Noncontact Gesture Recognizing
Tang, Y., Zhou, H., Sun, X., Diao, N., Wang, J., Zhang, B., Qin, C., Liang, E., & Mao, Y. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201907893 · Cited by 266
Self-citationQu, Z., Huang, M., Chen, C., An, Y., Liu, H., Zhang, Q., Wang, X., Liu, Y., Yin, W., & Li, X. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202202048 · Cited by 88
Metadata
- Authors
- Zhang, B., Tang, Y., Dai, R., Wang, H., Sun, X., Qin, C., Pan, Z., Liang, E., & Mao, Y.
- Venue
- Nano Energy
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 64
- Pages
- 103953
- ISSN
- 2211-2855 (ISSN-L)
- OpenAlex
- W2965209292
- Cited by
- 239
- References
- 2
- FWCI
- 13.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, B., Tang, Y., Dai, R., Wang, H., Sun, X., Qin, C., Pan, Z., Liang, E., & Mao, Y. (2019). Breath-based human–machine interaction system using triboelectric nanogenerator. Nano Energy. https://doi.org/10.1016/j.nanoen.2019.103953
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