3D Depth Camera Animal Behavior Classification

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Solution Overview

Problem

Current methods for quantifying animal behavior are unreliable and subjective, particularly in mammals, as they rely on human perception and are limited by two-dimensional video analysis, failing to capture complex three-dimensional pose dynamics and spatiotemporal organization of behavior, which hinders the understanding of behavioral modules and their adaptation.

Innovation Solution

A system using a three-dimensional depth camera to process video recordings, extracting multi-dimensional data points from animal posture, and applying Bayesian inference and Hidden Markov Models to identify and classify behavioral modules objectively, without prior definitions, revealing sub-second regularities in behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If human observers manually quantify animal behavior, then behavioral data can be collected, but reliability and reproducibility decrease due to subjectivity and human perception limitations

Engineering Contradiction:

Improvereliability of behavioral dataVSAvoidsubjectivity of behavior classification

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent replaces human manual observation and classification systems with an automated computer vision system. The system uses depth cameras to capture three-dimensional pose data, processes it through computational algorithms, and objectively classifies behavioral modules without human subjectivity, thereby improving reliability and reproducibility of behavioral quantification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms behavioral analysis from subjective human categorization to objective quantitative measurement. By converting behavior into multi-dimensional pose data points and analyzing them through computational models, the system creates reproducible, measurable parameters that eliminate the variability inherent in human observation

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If two-dimensional video analysis is used, then video can be captured, but complex three-dimensional pose dynamics and spatiotemporal organization of behavior cannot be captured

Engineering Contradiction:

Improveprecision of behavior quantificationVSAvoiddimensionality of video analysis

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transitions from two-dimensional video analysis to three-dimensional pose dynamics analysis by incorporating depth information. The system uses depth cameras to capture spatial coordinates, orientations, and temporal variations of animal poses, enabling precise quantification of complex three-dimensional behaviors that cannot be measured in two dimensions

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Loss of time

If human observers classify behaviors, then behavioral categories can be assigned, but behaviors at fast timescales relevant to neural activity cannot be adequately characterized

Engineering Contradiction:

Improvetimescale of behavior analysisVSAvoidaccuracy of behavior characterization

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The patent implements continuous, high-frequency sampling of behavioral data at the millisecond scale, maintaining continuous observation throughout the entire behavioral sequence. This continuous acquisition at fast timescales allows the system to capture rapid neural-related behaviors without the temporal resolution limitations of manual observation

Inventive Principle:

Principle #20Continuity of useful action

4Extent of automation

If automated computer programs analyze behavior, then some automation is achieved, but behaviors composed of distinct patterns of motion at multiple timescales cannot be fully characterized

Engineering Contradiction:

Improveautomation of behavior analysisVSAvoidresolution of spatiotemporal behavior structure

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The patent segments behavior into discrete temporal bins at multiple timescales, allowing the system to analyze and characterize distinct patterns of motion at different resolution levels. This segmentation enables simultaneous characterization of fast neural-related behaviors and slower behavioral sequences, providing comprehensive coverage across the full range of temporal scales

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11944429B2Automatically classifying animal behavior

Publication Date: 2024.04.02 PRESIDENT & FELLOWS OF HARVARD COLLEGE

AI summary

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself— rather than using a priori definitions for what should constitute a measurable unit of action— identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.