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Machine Learning Algorithm Revolutionizes How Scientists Study Behavior

CMU's Erik Yttri and Alex Hsu

CMU's Erik Yttri and Alex Hsu say their algorithm identifies behavior without user bias.

Credit: Carnegie Mellon University

Carnegie Mellon University's Eric Yttri and Alex Hsu have designed an unsupervised machine learning algorithm to simplify and fine-tune behavioral study. The B-SOiD algorithm finds behaviors by identifying patterns in the position of an animal's body, and can tell researchers what behavior is occurring at every frame in a video using computer vision software.

The researchers describe the tool in "B-SOiD, An Open-Source Unsupervised Algorithm for Identification and Fast Prediction of Behaviors," published in Nature Communications.

"It uses an equation to consistently determine when a behavior starts," says Hsu. "Once you reach that threshold, the behavior is identified, every time."

B-SOiD eliminates user bias as well as time cost and painstaking work, Yttri says. "We can accurately process hours of data in a matter of minutes," he says.

From Carnegie Mellon University
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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