Evaluation of unsupervised learning algorithms for the classification of behavior from pose estimation data
Summary: Analyzing animal behavior is crucial for decoding brain function, modeling neurological disorders, and assessing therapeutics. Recent advances in pose-estimation tools like DeepLabCut and SLEAP have revolutionized behavioral analysis by enabling precise tracking of animal body movements. Ho...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Patterns |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666389925000856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Summary: Analyzing animal behavior is crucial for decoding brain function, modeling neurological disorders, and assessing therapeutics. Recent advances in pose-estimation tools like DeepLabCut and SLEAP have revolutionized behavioral analysis by enabling precise tracking of animal body movements. However, these tools do not automate behavioral classification. Unsupervised learning algorithms address this gap by identifying clusters of recurring behavioral motifs from pose-tracking data without requiring pre-labeled datasets, reducing observer bias and uncovering novel patterns. This study compares four recent unsupervised learning algorithms—B-SOiD, BFA, VAME, and Keypoint-MoSeq—analyzing their methodological approaches, clustering efficiency, and ability to produce meaningful behavioral classifications. By offering a qualitative and quantitative evaluation, this paper aims to aid researchers in selecting the most suitable tool for their specific research needs. The bigger picture: Understanding animal behavior is crucial for decoding how the brain works, modeling neurological disorders, and assessing treatments. Recent technological advances have opened new opportunities to capture and classify animal movements and behaviors with unprecedented accuracy. By bridging the gap between movement tracking and behavior classification, these tools promise to revolutionize neuroscience by providing scalable, unbiased methods for behavioral analysis and enabling researchers to uncover previously unknown patterns in animal behavior. However, these tools are new and largely unvalidated, with no objective metrics available to determine the best tool for a given task. This study aims to provide a better understanding of these algorithms, offering qualitative and quantitative comparisons to aid researchers in selecting the most suitable tool for their specific research needs. |
|---|---|
| ISSN: | 2666-3899 |