Timing matters: The impact of stereo impairment onset on motor movement. Godinez et al.
Description
-This csv file named Figure_1-3 contains the monocular-to-binocular ratio for each subject, shape, and distance for each kinematic variable. It also contains the stereoacuity (sa) and the binocular visual acuity (vaOULog) for each subject. This dataset was used to create figures 1-3. -The folder raw data contains the data for each subject, condition, and trial with each subject as a separate folder. Inside each folder, the csv files are named subjectID_condition_trialNumber. b is for binocular and D is for the monocular condition. The csv files contain raw sensor data with columns 1-3 pertaining to x, y, z position for sensor 1 (thumb), columns 4-6 are for sensor 2 (gripping finger, either index or middle), columns 7-9 for sensor 3 (wrist). - The csv file participant_shape_distance contains parameter information for each trial with Columns: subject, group, condition, trial, shape, and distance.
Files
Steps to reproduce
We analyzed six kinematic variables: peak velocity (PV), maximum grip aperture (MGA), time to maximum grip aperture (tMGA), grip-closure time (GCT), deceleration phase (Deceleration) and peg-placement time (PPT). The velocity trace, which was calculated as the average displacement of the thumb and finger sensors over time. From velocity, we calculated PV— the maximum velocity before object pickup. Deceleration, defined as the time segment between PV and object pick-up, represents the low-velocity phase as the participant is nearing the object and closing the grip. The grip aperture trace, which was calculated as the 3D distance between two sensors (thumb and finger). MGA, defined as the maximum grip aperture before object pick-up, represents the largest scaling of the hand before object pick-up. tMGA, defined as the segment between PV and MGA, was calculated as the time segment between global maximum velocity before object-pick-up to global maximum-grip aperture before object pick-up. GCT represents the duration to apply the grip from MGA to object pick-up. Object pick-up and drop-off were calculated using the velocity trace and a dynamically changing search window and threshold, initially set at 5 cm/sec (Grant et al., 2007; Melmoth et al., 2009). We tried different methods and settled on this method since it accounts for differences in the time participants took on the task and the speed at which participants picked up or dropped off the peg. To determine object pick-up and drop-off times, we examined the velocity segment starting from MGA and ending in the velocity maximum of the second half of the velocity trace, which indicates the velocity increase after dropping the peg and moving the hand away. We split the trace into 10 equal parts, which served as search window segments. A rolling window of 30 frames determined the global minimum within the first search segment (1/10) of the velocity trace. If the global minimum was less than the threshold, it was recorded as the object pick-up time. However, if the minimum was greater than the threshold, the search window was expanded to two segments (2/10) and the process repeated. If after searching three segments (3/10), the minimum threshold was not reached, the threshold increased by 1 cm/s and the search process started again with the first segment. Object drop-off was calculated with the same method except the velocity trace was reversed (starting from velocity maximum of the second half to MGA). Each graph was visually inspected, and points were corrected if they showed clear errors (only 6% of 1,330 points needed correction). PPT was then defined as the time segment between object pick-up and object drop-off.