Closing the loop in legged neuromechanics: An open-source computer vision controlled treadmill
Andre Spence and colleagues developed a closed-loop platform for studying locomotor behavior on treadmills. This tool is among several useful methods for studying locomotor behavior that is available from the Spence Lab at Temple University. The platform allowing for controlled variations of behavioral state and includes methods for video tracking and feedback control. The treadmill is controlled by Python software, allowing for flexible control of video tracking parameters, behavioral triggers, distance ran, and feedback. In their paper, Spence and colleagues analyzed video data using Kalman filters to measure locomotor velocity and position on the treadmill. The authors describe many options for using the system, including real-time estimations of the behavioral measures for closed-loop control.
This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID: SCR_023511
Special thanks to Yanabi Sierra, an undergraduate neuroscience major, for providing this project summary! This summary is part of a collection from students in a Computational Methods for Neuroscience Course at American University.
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