LEAP: An Automated System to Measure Animal Body Part Dynamics

Web Published:

LEAP: An Automated System to Measure Animal Body Part Dynamics


Princeton Docket # 19-3510


Analysis of lab animal movements is a key element in a variety of behavioral studies. The current standard for analyzing animal movement relies on manual video tracking, which is time consuming, mentally taxing, and error prone. Faced with these challenges, researchers at Princeton University have developed fast animal pose estimation software called LEAP (LEAP Estimates Animal Pose) that uses deep neural networks and requires minimal input for network training. After training on as few as 10 manually labeled images, LEAP can process videos of behaving animals and provide a full set of geometrical coordinates for each body part over time. This is a major improvement over existing available software that provides generalized behavioral information, but lack full body position information. LEAP generates accurate detailed descriptions of animal movements with the benefits of speed and facile operation. In addition, LEAP’s flexible architecture allows for variation in imaging settings. The program has been proven to accurately track body part movements of a diverse set of animals including fruit flies, mice, and giraffes and is currently undergoing modifications for the analysis of multiple animals simultaneously. LEAP’s inventors are seeking a partner to further develop the software into a highly user friendly format that will be licensed to laboratories for in-house use. 



       High throughput analysis of animal behaviors (e.g. drug screens)

       Analysis of individual body part movements

       Detailed analysis of complex behaviors



       Fast learning accurate neural network

       Sensitive analysis

       Flexible imaging settings

       No programming skills required


Intellectual Property & Development Status

Patent protection is pending.

Princeton is currently seeking commercial partners for the further development and commercialization of this opportunity.



Pereira, T., Aldarondo, D., Willmore, L., Kislin, M., Wang, S., Murthy, M., Shaevitz, J. Fast animal pose estimation using deep neural networks. BioRXiv DOI: 10.1101/331181 (2018)


The Inventors

Joshua Shaevitz is a professor of Physics and Biophysics at the Lewis-Sigler Institute for Integrative Genomics, the Department of Molecular Biology, and the Princeton Neuroscience Institute at Princeton University. His research interests focus on the determinants of cell shape, formation of complex cellular patterns, and how behaviors are organized by the brain. He received a Ph.D. and M.S. in Physics from Stanford University and a B.A. in Physics from Columbia University. He has received numerous awards including the Presidential Early Career Award for Scientists and Engineers, the NSF CAREER Award, and the Human Frontier Science Program Young Investigator Award among others.


Mala Murthy is an associate professor of Molecular Biology and the Princeton Neuroscience Institute at Princeton University. Her research interests focus on how the brain converts sensory stimuli into meaningful representations and how they drive behavioral responses. Murthy received a Ph.D. in Neuroscience from Stanford University and an S.B. in Biology from Massachusetts Institute of Technology. She is an HHMI Faculty Scholar and has received numerous awards including an NIH New Innovator award, an NSF CAREER award, and a McKnight Foundation Scholar award among others.


Talmo Pereira is a graduate student co-advised by Joshua Shaevitz and Mala Murthy at Princeton University.


Diego Aldarondo was an undergraduate in the laboratory of Mala Murthy at Princeton University. Diego graduated in 2018.




Laurie Tzodikov

Princeton University Office of Technology Licensing

(609) 258-7256 • tzodikov@princeton.edu


Catherine Ruesch

Princeton University Office of Technology Licensing

University Administrative Fellow



Patent Information:
Computers and Software
For Information, Contact:
Laurie Tzodikov
Licensing Associates
Princeton University
Joshua Shaevitz
Mala Murthy
Talmo Pereira
Diego Aldarondo