Real-Time Analysis of Animal Feeding Behavior With a Low-Calculation-Power CPU

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 4; pp. 1197 - 1205
• Main Authors: Totani, Yuki, Kotani, Susumu, Odai, Kei, Ito, Etsuro, Sakakibara, Manabu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> Lymnaea stagnalis</tex-math> </inline-formula> </named-content>; Algorithms; Animal behavior; Animal bites; Animals; Automation; Aversion; Central processing units; conditioned taste aversion; CPUs; Feeding behavior; Food; Image recognition; Learning; Long term memory; low-calculation-power; Machine learning; Mathematical analysis; Mouth; Object recognition; Real time; Real-time analysis; Real-time systems; Sugar; Supervised learning
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2933243

Our goal was to develop an automated system to determine whether animals have learned and changed their behavior in real-time using a low calculation-power central processing unit (CPU). The bottleneck of real-time analysis is the speed of image recognition. For fast image recognition, 99.5% of the image was excluded from image recognition by distinguishing between the subject and the background. We achieved this by applying a binarization and connected-component labeling technique. This task is important for developing a fully automated learning apparatus. The use of such an automated system can improve the efficiency and accuracy of biological studies. The pond snail Lymnaea stagnails can be classically conditioned to avoid food that naturally elicits feeding behavior, and to consolidate this aversion into long-term memory. Determining memory status in the snail requires real-time analysis of the number of bites the snail makes in response to food presentation. The main algorithm for counting bites comprises two parts: extracting the mouth images from the recorded video and measuring the bite rate corresponding to the memory status. Reinforcement-supervised learning and image recognition were used to extract the mouth images. A change in the size of the mouth area was used as the cue for counting the number of bites. The accuracy of the final judgment of whether or not the snail had learned was the same as that determined by human observation. This method to improve the processing speed of image recognition has the potential for broad application beyond biological fields.