Innovative Approach to Fish Stress Analysis: AI-Enhanced Biosensing and Accelerometer Integration
Authors: Horiguchi Yusuke , WU Haiyun , Murata Masataka , Matsumoto Haruto , Ohnuki Hitoshi , Shibata Takayuki , Endo Hideaki 
Published: December 2024

Background and Objectives:

In our collaborative efforts, we have pioneered the development of a wireless biosensor system that can monitor blood glucose levels in fish, serving as a indicator of stress response. However, while the response value of the biosensor detected the increase in the fish's blood glucose level, it was a challenge to pinpoint the exact factors causing it. To address this, we augmented our measurements with data from a triaxial acceleration sensor and posture estimation using deep learning. This integration of biosensors, physical sensors, and video analysis technology, a testament to the power of interdisciplinary research, allowed us to delve deeper into the state of the fish.

Materials and Methods:

The glucose biosensor, constructed using a Pt/Ir wire (φ: 0.178 mm) as the working electrode, Ag/AgCl as the reference electrode, and glucose oxidase immobilized on the working electrode surface, was connected to a data logger with a built-in triaxial acceleration sensor. This biosensor was then implanted in a Nile tilapia (Oreochromis niloticus), and the data logger was attached to the fish's side. After allowing the fish to swim freely in the experimental tank overnight, it was introduced to a larger individual, and its stress responses and acceleration due to fighting behavior were recorded. The confrontation was filmed from above the tank, and the positional coordinates of each individual in the filmed images were obtained using DeepLabCut.

Results and Discussion:

The response value of the biosensor of the individual that became subordinate in the tank from the middle of the confrontation increased. It is thought this was a stress response to being attacked by a larger individual and cornered against the wall in the tank. Acceleration also captured fluctuations derived from the intense fighting behavior during the confrontation. The positional coordinates of each individual reflected the power relationship between the larger and smaller individuals. The larger individual was often located near the center of the tank because of its dominance in the water tank.

In contrast, the smaller, subordinate individual was often located near the wall and occasionally pushed against the wall by the larger individual. In this experiment, we analyzed one-hour-long videos during the confrontation to observe the positional relationship between the two individuals from the beginning to the end. Combined with detecting stress responses by biosensors, the possibility of exploring which fish behaviors are accompanied by changes in physiological conditions while the fish are swimming has become apparent.

Conclusion:

Data from physical sensors such as triaxial accelerometers and video analysis can enhance the information from biosensor response values. The fighting behavior of fish and the social relationships among fish in the tank were quantified by accelerometers and video analysis, shedding light on the causes of fluctuations in fish blood glucose levels. Given the close relationship between fish behavior and physiological state, the integration of physical sensor and video analysis technology with biosensor technology holds promise for improving fish health management in the field. Moreover, by experimentally correlating the fluctuations in biosensor values with the behavior of the fish in the video, it may be possible in the future to estimate the physiological state of the fish simply by monitoring the fish with a camera, a potential game-changer in fish health monitoring.

Figure 1