Department of Physics, Rajiv Gandhi University, Doimukh, Arunachal Pradesh 791112, India.
Department of Computer Science and Engineering, Rajiv Gandhi University, Doimukh, Arunachal Pradesh 791112, India.
Nanotechnology. 2023 Aug 17;34(44). doi: 10.1088/1361-6528/acec7b.
Here we report the development of triboelectric nanogenerator (TENG) based self-powered human motion detector with chemically developed Au-g-CN/ZnO based nanocomposite on common cellulose paper platform. Compared to bare g-CN, the nanocomposite in the form of hierarchical morphology is found to exhibit higher output voltage owing to the contribution of Au and ZnO in increasing the dielectric constant and surface roughness. While generating power ∼3.5W cmand sensitivity ∼3.3 V N, the flexible TENG, is also functional under common biomechanical stimuli to operate as human body movement sensor. When attached to human body, the flexible TENG is found to be sensitive towards body movement as well as the frequency of movement. Finally upon attaching multiple TENG devices to human body, the nature of body movement has been traced precisely using machine learning (ML) techniques. The execution of the learning algorithms like artificial neural network and random forest classifier on the data generated from these multiple sensors can yield an accuracy of 99% and 100% respectively to predict body movement with great deal of precision. The exhibition of superior sensitivity and ML based biomechanical motion recognition accuracy by the hierarchical structure based flexible TENG sensor are the prime novelties of the work.
在这里，我们报告了基于摩擦纳米发电机（TENG）的自供电人体运动探测器的开发，该探测器在普通纤维素纸平台上采用化学法制备的 Au-g-CN/ZnO 基纳米复合材料。与裸 g-CN 相比，由于 Au 和 ZnO 的贡献，增加了介电常数和表面粗糙度，分层形态的纳米复合材料表现出更高的输出电压。这种灵活的 TENG 在产生约 3.5W cm 的功率和 3.3 V N 的灵敏度的同时，还可以在常见的生物力学刺激下作为人体运动传感器运行。当附着在人体上时，柔性 TENG 对人体运动以及运动频率都很敏感。最后，通过将多个 TENG 设备附着到人体上，使用机器学习 (ML) 技术可以精确地跟踪人体运动的性质。在从这些多个传感器生成的数据上执行人工神经网络和随机森林分类器等学习算法，可以分别产生 99%和 100%的准确率，从而非常精确地预测人体运动。基于分层结构的灵活 TENG 传感器在生物力学运动识别方面的卓越灵敏度和基于 ML 的准确性是这项工作的主要新颖之处。
