Wang Qian, Li Mingming, Guo Pingping, Gao Liang, Weng Ling, Huang Wenmei
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Tianjin, 300130, China.
The Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300130, China.
Sci Rep. 2024 Sep 6;14(1):20781. doi: 10.1038/s41598-024-70661-7.
The sensor that simultaneously perceives bending strain and magnetic field has the potential to detect the finger bending state and hand position of the human and robot. Based on unique magneto-mechanical coupling effect of magnetostrictive materials, the proposed a bi-perceptive flexible sensor, consisting of the Co-Fe film and magnetic sensing plane coils, can realize dual information perception of strain/magnetic field through the change of magnetization state. The sensor structure and interface circuit of the sensing system are designed to provide high sensitivity and fast response, based on the input-output characteristics of the simulation model. An asynchronous multi-task deep learning method is proposed, which takes the output of the position task as the partial input of the bending state task to analyze the output information of the sensor quickly and accurately. The sensing system, integrating with the proposed model, can better predict the bending state and approach distance of human or robot hand.
同时感知弯曲应变和磁场的传感器有潜力检测人类和机器人的手指弯曲状态及手部位置。基于磁致伸缩材料独特的磁机械耦合效应,所提出的一种双感知柔性传感器由钴铁薄膜和磁感平面线圈组成,可通过磁化状态的变化实现应变/磁场的双信息感知。基于仿真模型的输入输出特性,设计了传感系统的传感器结构和接口电路,以提供高灵敏度和快速响应。提出了一种异步多任务深度学习方法,该方法将位置任务的输出作为弯曲状态任务的部分输入,以快速准确地分析传感器的输出信息。与所提出的模型集成的传感系统能够更好地预测人类或机器人手部的弯曲状态和接近距离。