3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning
Abstract: Wearable sensors have garnered considerable attention due to their flexibility and lightweight characteristics in the realm of healthcare applications. However, developing robust wearable sensors with facile fabrication and good conformity remains a challenge. In this study, a conductive graphene nanoplate‐carbon nanotube (GC) ink is synthesized for multi jet fusion (MJF) printing. The layer‐by‐layer fabrication process of MJF not only improves the mechanical and flame‐retardant properties of the printed GC sensor but also bolsters its robustness and sensitivity. The direction of sensor bending significantly impacts the relative resistance changes, allowing for precise investigations of joint motions in the human body, such as those of the fingers, wrists, elbows, necks, and knees. Furthermore, the data of resistance changes collected by the GC sensor are utilized to train a support vector machine with a 95.83% accuracy rate for predicting human motions. Due to its stable humidity sensitivity, the sensor also demonstrates excellent performance in monitoring human breath and predicting breath modes (normal, fast, and deep breath), thereby expanding its potential applications in healthcare. This work opens up new avenues for using MJF‐printed wearable sensors for a variety of healthcare applications.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning ; day:08 ; month:11 ; year:2023 ; extent:13
Advanced science ; (08.11.2023) (gesamt 13)
- Creator
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Hou, Yanbei
Gao, Ming
Gao, Jingwen
Zhao, Lihua
Teo, Edwin Hang Tong
Wang, Dong
Qi, H. Jerry
Zhou, Kun
- DOI
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10.1002/advs.202304132
- URN
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urn:nbn:de:101:1-2023110914053394234708
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:57 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Hou, Yanbei
- Gao, Ming
- Gao, Jingwen
- Zhao, Lihua
- Teo, Edwin Hang Tong
- Wang, Dong
- Qi, H. Jerry
- Zhou, Kun
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