解耦(概率)
跟腱
计算机科学
可穿戴计算机
压力传感器
机器人学
灵敏度(控制系统)
可穿戴技术
人工智能
生物医学工程
肌腱
模拟
电子工程
机器人
工程类
嵌入式系统
控制工程
机械工程
医学
解剖
作者
Zihan Wang,Shenlong Wang,Boling Lan,Yuebing Sun,Longchao Huang,Yong Ao,Xuelan Li,Long Yi Jin,Weiqing Yang,Weili Deng
标识
DOI:10.1007/s40820-025-01757-6
摘要
Abstract Bimodal pressure sensors capable of simultaneously detecting static and dynamic forces are essential to medical detection and bio-robotics. However, conventional pressure sensors typically integrate multiple operating mechanisms to achieve bimodal detection, leading to complex device architectures and challenges in signal decoupling. In this work, we address these limitations by leveraging the unique piezotronic effect of Y-ion-doped ZnO to develop a bimodal piezotronic sensor (BPS) with a simplified structure and enhanced sensitivity. Through a combination of finite element simulations and experimental validation, we demonstrate that the BPS can effectively monitor both dynamic and static forces, achieving an on/off ratio of 1029, a gauge factor of 23,439 and a static force response duration of up to 600 s, significantly outperforming the performance of conventional piezoelectric sensors. As a proof-of-concept, the BPS demonstrates the continuous monitoring of Achilles tendon behavior under mixed dynamic and static loading conditions. Aided by deep learning algorithms, the system achieves 96% accuracy in identifying Achilles tendon movement patterns, thus enabling warnings for dangerous movements. This work provides a viable strategy for bimodal force monitoring, highlighting its potential in wearable electronics.
科研通智能强力驱动
Strongly Powered by AbleSci AI