Neural Network Behavior Classification for Test Subjects
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Solution Overview
Problem
Current systems for automating behavioral analysis of test subjects, such as mice, zebrafish, and birds, are inefficient and prone to errors due to reliance on human observation and problem-dependent algorithms that fail to adapt to experimental setup changes, leading to a need for more robust and accurate automated classification methods.
Innovation Solution
A convolutional neural network system that captures and annotates video of test subjects, capable of processing one minute of video in twelve seconds, using a combination of convolutional and recurrent layers, along with shape and motion data processing, to learn end-to-end behavior classification, adapting to environmental changes and generalizing across different experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observation is used for behavioral analysis, then accuracy can be maintained at human level, but processing time becomes excessively long and productivity is severely limited
Solution Approach 1:
The patent replaces the mechanical human observation system with an automated computer vision system using convolutional neural networks. The CNN processes video footage of test subjects automatically, achieving human-level accuracy in behavior classification while dramatically increasing processing throughput. The system substitutes human visual perception and cognitive judgment with machine learning-based image recognition algorithms.
2Extent of automation
If problem-dependent algorithms are used for automation, then initial processing can be achieved, but the system becomes susceptible to failure from small changes in experimental setup and lacks robustness
Solution Approach 1:
The patent employs a convolutional neural network that learns optimal parameters and features directly from training data rather than relying on hand-crafted, problem-dependent algorithms. The CNN automatically adapts to different experimental setups by learning from diverse training examples, making the system robust to variations in environment, lighting, camera angles, and subject appearance without requiring manual retuning.
Solution Approach 2:
The neural network system is designed to be universally applicable across different experimental conditions and test subject types. By training on diverse data and using transfer learning capabilities, the same CNN architecture can process videos from different cameras, environments, and experimental setups without requiring problem-specific algorithm modifications.
3Extent of automation
If traditional computer vision techniques are used, then some automation is achieved, but the system requires extensive tuning to specific environments and lacks generalization capability
Solution Approach 1:
The system performs preliminary learning during a training phase where the CNN is exposed to numerous example videos from various environments and experimental setups. This pre-training allows the network to learn invariant features and patterns that generalize across different conditions, enabling the system to adapt to new environments without requiring extensive retuning when deployed.
Data Source
AI summary
The method and system includes a plurality of test subject containers, each having a test subject therein. A plurality of video cameras is focused on the test subject containers to capture video of the behavior of each of the test subjects. The system and method also includes a storage system for storing the plurality of video from the cameras. The storage system may be local or cloud-based as is known in the art. The system further has one or more computers with a neural network configured to (a) retrieve the video from the storage system of the test subjects, (b) analyze the video to identify a plurality of observable behaviors in the test subjects, (c) annotate the video with the observed behavior classifications, and (d) store the annotated video in the storage system.