Self‐Spiking Linear Neuromorphic Soft Pressure Sensor for Underwater Sensing Applications
Abstract
Abstract Many aquatic vertebrates rely on neuromasts in their lateral line system to detect water vibrations and pressure gradients. These neuromasts contain specialized hair cells that function as mechanoreceptors, converting mechanical stimuli into electric signals for brain processing. While neuromorphic sensors can emulate biologic sensory systems, they are facing significant challenges in underwater stability and complex data processing. Here, a bioinspired neuromorphic soft pressure sensor designed for stable underwater performance and simplified signal processing is proposed. This is achieved through a novel integration of micro‐magnetic spheres, a microfluidic channel, and alternating coil connections. This sensor exhibits self‐spiking behavior upon applied force with high linearity ( R 2 = 0.997) in response to pressure changes up to 200 kPa. The proposed mechanism generates distinct magnetic action potentials via its alternating coil design, enabling efficient signal processing. This artificial neuromast achieves 92.19% accuracy in game control applications and 94.71% accuracy in underwater object recognition using machine learning. Additionally, the sensor was validated in both experimental ocean basins and open‐sea environments, confirming its potential for underwater robotics, ocean environmental monitoring, and marine industrial applications.
Article Details
Authors (18)
Jingyi Yang
Si Li
Department of Chemical and Biomolecular Engineering
Hian Hian See
Department of Materials Science and Engineering National University of Singapore Singapore 117575 Singapore
Kelu Yu
Aeree kim
Jacob Kaihong Lim
Institute of Innovation in Health Technology (iHealthtech) National University of Singapore Singapore 119276 Singapore
Benjamin Tse
Institute of Innovation in Health Technology (iHealthtech) National University of Singapore Singapore 119276 Singapore
Shiwei Yang
Yan Zhi Tan
Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore
Xinzhi Zhang
Xuanyi Zhou
Evolution Innovation Laboratory Department of Biomedical Engineering National University of Singapore Singapore 119276 Singapore
Quan Xiong
Department of Biomedical Engineering National University of Singapore Singapore 117583 Singapore
Yi En Kou
Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore
Linkun Liu
Department of Materials Science and Engineering National University of Singapore Singapore 117575 Singapore
Eng Wei Goh
Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore
Marcelo H. Ang
Department of Mechanical Engineering National University of Singapore Singapore 117575 Singapore
Raye Chen‐Hua Yeow
Evolution Innovation Laboratory Department of Biomedical Engineering National University of Singapore Singapore 119276 Singapore
Benjamin C. K. Tee