Abstract
Flexible wearable sensors can assist patients with physical injuries or disabilities in auxiliary treatment and rehabilitation, which are of great importance to the development of the future medical field. Most flexible wearable sensors convert physiological signal changes or the changes of body states caused by motion into electrical signals to realize human motion information sensing. Two typical examples are EMG sensors and angle sensors. However, the existing EMG electrodes have many disadvantages such as high manufacturing cost and inferior contact with skin, which makes it impossible to guarantee a stable signal acquisition when the body is in kinetic state. Moreover, angle sensors need to develop in the direction of high sensitivity, ease of use and low cost. Graphene has thus entered the field of vision of researchers. In this work, we tested and analyzed two flexible sensors based on graphene. Firstly, we prepared graphene flexible electrodes and performed human sEMG sensing tests. Meanwhile, we proposed a graphene-based strain gauge with grid structure and performed angle sensing tests. The experimental results show that the graphene electrodes can effectively monitor human movement information such as blink and arm movement with high sensitivity. The graphene grid strain gauge is able to detect flexion angle of joints with high linearity from 20° to 90°. As a flexible sensing material, graphene has the characteristics of high sensitivity, repeatability and ease of use, and can be widely used in different types of sensing, which means that graphene may become one of the prime materials for future wearable sensors.
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Acknowledgments
This work is supported by the Wuhan Application Frontier Project under Grant 2020020601012220 and the National Natural Science Foundation of China under Grant 52075398.
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Sun, W., Liu, Q., Luo, Q., Ai, Q., Meng, W. (2022). Flexible Lightweight Graphene-Based Electrodes and Angle Sensor for Human Motion Detection. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_14
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DOI: https://doi.org/10.1007/978-3-031-13835-5_14
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