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Piezotronic Sensor for Bimodal Monitoring of Achilles Tendon Behavior

Zihan Wang, Shenlong Wang, Boling Lan, Yuebing Sun, Longchao Huang, Yong Ao, Xuelan Li, Long Yi Jin, Weiqing Yang, Weili Deng

Year
2025
Citations
22
Access
Open access

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.

Keywords

Decoupling (probability)Achilles tendonComputer scienceWearable computerPressure sensorRoboticsSensitivity (control systems)Wearable technologyArtificial intelligenceBiomedical engineering

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