Self-Spiking Linear Neuromorphic Soft Pressure Sensor for Underwater Sensing Applications.

Area of Science:

• Biomimetics and Sensor Technology
• Neuroengineering
• Marine Robotics

Background:

• Aquatic vertebrates use neuromasts in their lateral line system for mechanoreception.
• Existing neuromorphic sensors struggle with underwater stability and complex data processing.
• A bioinspired approach is needed to overcome these limitations.

Purpose of the Study:

• To develop a bioinspired neuromorphic soft pressure sensor for stable underwater applications.
• To simplify signal processing for underwater sensory data.
• To enhance the capabilities of underwater robotics and monitoring systems.

Main Methods:

• Integration of micro-magnetic spheres and a microfluidic channel.
• Utilizing alternating coil connections for signal generation.
• Designing a sensor with self-spiking behavior for pressure detection.

Main Results:

• The sensor demonstrated high linearity (R² = 0.997) for pressure changes up to 200 kPa.
• Distinct magnetic action potentials were generated, enabling efficient signal processing.
• Achieved high accuracy (92.19% - 94.71%) in game control and underwater object recognition tasks.

Conclusions:

• The proposed artificial neuromast offers a stable and efficient solution for underwater sensing.
• Validated in diverse aquatic environments, confirming its practical applicability.
• Shows significant potential for advancing underwater robotics, ocean monitoring, and marine industries.