Mice will even spontaneously reel in a string, but the behavior becomes more reliable when reinforced by a food reward attached to the string ( Laidre, 2008). If animals are presented with an overhanging string, they adopt a standing or sitting posture and use hand-over-hand movements to reel in the string ( Blackwell et al., 2018a Blackwell et al., 2018b Blackwell et al., 2018c). ![]() The task has been used to assess cognitive function, motivation, brain and spinal cord injury and disease consequences and work/reward relations in animals and as a therapy and exercise tool in humans. Variations of the behavior are observed in over 160 species of animals including birds, bees, rodents and primates and humans ( Jacobs and Osvath, 2015 Singh et al., 2019). String-pulling is a proto-tool behavior in which an animal pulls on a string to obtain a reward. The utility of the task and software is demonstrated by characterizing postural and hand kinematic differences in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster. Based on image-segmentation and heuristic algorithms for object tracking, the software also allows tracking of body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, and path and speed of hand movements. Here, we describe a Matlab-based software that allows whole-body motion characterization using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Typical analysis includes kinematic assessment of hand movements done by manually annotating frames. OOSM decreases as the delay becomes longer.String-pulling by rodents is a behavior in which animals make rhythmical body, head, and bilateral forearm as well as skilled hand movements to spontaneously reel in a string. Note that the effect of the uncertainty reduction using an Increasing the value of this property increases the amount of memory that mustīe allocated for the state history, but enables you to process OOSMs that arriveĪfter longer delays. Reduce the uncertainty of the estimated state. You can use the OOSM and the retrodict (Sensor Fusion and Tracking Toolbox) and retroCorrect (Sensor Fusion and Tracking Toolbox) (or retroCorrectJPDA (Sensor Fusion and Tracking Toolbox) for multiple OOSMs) object functions to State covariance history up to the last N+1Ĭorrections. N>1, the filter object saves the past state and Sensor Fusion and Tracking Toolbox license. Retrodiction capability of the filter object. Setting this property to a positive integer enables the OOSM Setting this property to 0 disables the OOSM Specify a custom motion model, the filter uses this state-space model: Optionally specify a control model matrix, B, as well. Model matrix H as input arguments to the Kalman filter. ![]() Specify a state transition model matrix A and a measurement If you specify MotionModel as "Custom", you must MeasurementNoise properties, respectively. Motion model, specify them in the ProcessNoise and Noise and measurement noise values different from the default values for the In the table, dt is the time step specified in Kronecker product of kron(eye(3),[dt^2/2 dt Kronecker product of kron(eye(2),[dt^2/2 dt īlock diagonal matrix with the [1 dt 0.5*dt^2 0 ![]() Kronecker product of kron(eye(3),[dt^2/2 īlock diagonal matrix with [1 dt dt^2/2 0 1 dt Kronecker product of kron(eye(2),[dt^2/2
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