Fig 1.
A: Experimental setup consisting of a monkey cage with four depth cameras. B: Schematic illustration of processing steps of the present MCS. A monkey was captured by four depth cameras (Cam1-4) (a, b), and the images were merged to make a 3D image of the monkey represented by 3D points on the entire surface of the monkey (b). Simultaneously captured color images were mapped onto the 3D points (c). Finally, a skeleton model of the monkey was fitted onto the 3D image (d). C: A skeletal model of a monkey used in the present study. The model consisted of spheres connected by joints. Centers of the spheres, where lines are connected, indicates joints. Number of degrees of freedom (DOF) in each joint is shown by color. D: Attraction force from the points. Small squares represent captured 3D points. Gray spheres represent spheres in the model. The red points attract the sphere i. E: Repulsive force from the points. Arrows indicate the surface normal at the points. The blue points push the sphere i away. Other descriptions are same as D.
Fig 2.
Examples of captured motion in the shuttling task.
A: Snapshots of the video captured in the task without obstacles (session 1), the task with obstructing bars at a low height in the middle of the cage (session 2), and the task with obstructing bars at a medium height in the middle of the cage (session 3). White solid lines indicate inner skeletons in the trunk and right limbs, dotted lines indicate inner skeletons in the left limbs. B and C: Traces of the estimated posture from the side view (B) and top view (C) based on the snapshots shown in A. Black bars and points represent the obstacle bars. Green lines, trunk; black lines, head; red lines, forelimbs; blue lines, hind limbs. The solid and dotted lines represent right and left limbs, respectively.
Table 1.
Video property analyzed (A), estimation cost for the MCS-assisted protocol (B), and absolute estimation errors between the MSC-assisted and manual protocols (C) when Experimenter 1 analyzed the videos in each session with the MCS-assisted protocol while Experimenter 2 analyzed the same videos in the manual protocol.
Fig 3.
Validation of behavioral event detection by the MCS.
A: Chronograms of behavioral events in the MCS-assisted estimation (MCS, blue) and manual estimation based on visual inspection (Exp, red) in session 2 (left) and session 3 (right). Note that there was no false positive nor false negative detection in the chronograms. The monkey displayed crawling once, but did not cross the bars in session 2. In the 6th trial in session 3 (arrow), the monkey crossed the bars without crawling, i.e., it passed between the bars. B: Correlation of the duration of behavioral events between MCS-assisted and manual estimation. Values in each graph indicate the correlation coefficient (r) and p-value of the correlation (p).
Fig 4.
Comparison of behavioral event detection errors between the two different experimenters in MCS-assisted and manual estimation in the shuttling task.
Estimation errors of onset and offset timings and duration in the shuttling task were compared. * Significant difference, p < 0.05 (paired t-test). Error bars represent SEMs.
Fig 5.
Effects of MAP on spontaneous behaviors.
A and B: Examples of the time course of head rotation speed (black line) and chest speed (gray line) of a monkey after administration of saline (A) and MAP (B). Thick black bars above the graph represent periods when the monkey was crouching. C-E: Comparison of motor activities between saline and MAP in the MCS-assisted estimation. * Significant difference, p < 0.05. + Tended toward significance, p < 0.1.
Fig 6.
Reproducibility of estimated data using the MCS in the MAP experiment.
A-C: Correlation of motor activities in the MAP experiment between the two different experimenters in MCS-assisted estimation.