Quantitative model of transport-aperture coordination during reach-to-grasp movements.
It has been found in our previous studies that the initiation of aperture closure during reach-to-grasp movements occurs when the hand distance to target crosses a threshold that is a function of peak aperture amplitude, hand velocity, and hand acceleration. Thus, a stable relationship between those four movement parameters is observed at the moment of aperture closure initiation. Based on the concept of optimal control of movements (Naslin 1969) and its application for reach-to-grasp movement regulation (Hoff and Arbib 1993), it was hypothesized that the mathematical equation expressing that relationship can be generalized to describe coordination between hand transport and finger aperture during the entire reach-to-grasp movement by adding aperture velocity and acceleration to the above four movement parameters. The present study examines whether this hypothesis is supported by the data obtained in experiments in which young adults performed reach-to-grasp movements in eight combinations of two reach-amplitude conditions and four movement-speed conditions. It was found that linear approximation of the mathematical model described the relationship among the six movement parameters for the entire aperture-closure phase with very high precision for each condition, thus supporting the hypothesis for that phase. Testing whether one mathematical model could approximate the data across all the experimental conditions revealed that it was possible to achieve the same high level of data-fitting precision only by including in the model two additional, condition-encoding parameters and using a nonlinear, artificial neural network-based approximator with two hidden layers comprising three and two neurons, respectively. This result indicates that transport-aperture coordination, as a specific relationship between the parameters of hand transport and finger aperture, significantly depends on the condition-encoding variables. The data from the aperture-opening phase also fit a linear model, whose coefficients were substantially different from those identified for the aperture-closure phase. This result supports the above hypothesis for the aperture-opening phase, and consequently, for the entire reach-to-grasp movement. However, the fitting precision was considerably lower than that for the aperture-closure phase, indicating significant trial-to-trial variability of transport-aperture coordination during the aperture-opening phase. Implications for understanding the neural mechanisms employed by the CNS for controlling reach-to-grasp movements and utilization of the mathematical model of transport-aperture coordination for data analysis are discussed.
Rand
Miya K
MK
Motor Control Laboratory, Department of Kinesiology, Arizona State University, Box 870404, Tempe, AZ 85287-0404, USA. rand@asu.edu
Shimansky
Y P
YP
Hossain
Abul B M I
AB
Stelmach
George E
GE
D000328:Adult; D001132:Arm / Q000294:innervation / Q000502:physiology*; D001696:Biomechanical Phenomena; D002490:Central Nervous System / Q000502:physiology; D005260:Female; D005385:Fingers / Q000502:physiology; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008297:Male; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D009949:Orientation / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D013028:Space Perception / Q000502:physiology; D013997:Time Factors