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Schweitzer, N., Apter, Y., Ben-David, J., Liebermann, D. G., & Parush, A. (1995). A field study of braking reactions during driving II: Minimum driver braking times. Ergonomics, 38(9), 1903–1910.
Abstract: The minimum total braking time (i.e. the braking reaction time plus the accelerator-to-brake movement time) plays an important role in defining a minimum following gap (MFG). This study was designed to obtain a lower limit for this gap. Total braking times (TBT) of a group of 51 male and female young athletes were monitored during real driving conditions. Sudden braking applied by a leading private passenger vehicle initiated the trials. A within-subject design was used to study the effects of different factors on braking time. Individuals performed a series of semi-counterbalanced trials at two following distances (6 and 12 m), two speeds (60 and 80 km/h) and three expectancy stages (naïve driving, partial knowledge, and full knowledge of the forthcoming manoeuvre). A three-way repeated measures ANOVA showed no major effects of ‘speed’, but major effects of the ‘expectancy’ and the ‘distance’ factors. The experiment yielded a mean TBT of 0·678 s (SD = 0·144 s) for trials averaged over distances and speeds in the naïve condition only. The data emphasize the role played by pre-cues in the braking response prior to emergency stops. Both the level of awareness of the forthcoming manoeuvre and the distance between vehicles appear to determine the response time. The descriptive statistics presented may also provide the basis for an objective, acceptable and legally valid minimum time gap for prosecution of ‘careless’ drivers.
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Liebermann, D. G., & Franks I.M. (2008). Video-feedback and information technologies. In I.M. Franks, & M. Hughes (Eds.), Essentials of notational analysis. E & FN Spon Pub.
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Friedman, J., SKM, V., Zatsiorsky, V. M., & Latash, M. L. (2009). The sources of two components of variance: an example of multifinger cyclic force production tasks at different frequencies. Exp Brain Res, 196(2), 263–277.
Abstract: In a multifinger cyclic force production task, the finger force variance measured across trials can be decomposed into two components, one that affects the combined force output (“bad variance”) and one that does not (“good variance”). Previous studies have found similar time patterns of “bad variance” and force rate leading to an approximately linear relationship between them. Based on this finding and a recently developed model of multifinger force production, we expected the “bad variance” during cyclic force production to increase monotonically with the rate of force change, both within a cycle and across trials at different frequencies. Alternatively, “bad variance” could show a dependence on task frequency, not on actual force derivative values. Healthy subjects were required to produce cyclic force patterns to prescribed targets by pressing on unidimensional force sensors, at a frequency set by a metronome. The task was performed with only the index finger, and with all four fingers. In the task with all four fingers, the “good variance” increased approximately linearly with an increase in the force magnitude. The “bad variance” showed within-a-cycle modulation similar to that of the force rate. However, an increase in the frequency did not lead to an increase in the “bad variance” that could be expected based on the natural relationships between action frequency and the rate of force change modulation. The results have been interpreted in the framework of an earlier model of multifinger force production where “bad variance” is a result of variance of the timing parameter. The unexpected lack of modulation of the “bad variance” with frequency suggests a drop in variance of the timing parameter with increased frequency. This mechanism may serve to maintain a constant acceptable level of variance under different conditions.
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Friedman, J., Latash, M. L., & Zatsiorsky, V. M. (2009). Prehension synergies: a study of digit force adjustments to the continuously varied load force exerted on a partially constrained hand-held object. Exp Brain Res, 197(1), 1–13.
Abstract: We examined how the digit forces adjust when a load force acting on a hand-held object continuously varies. The subjects were required to hold the handle still while a linearly increasing and then decreasing force was applied to the handle. The handle was constrained, such that it could only move up and down, and rotate about a horizontal axis. In addition, the moment arm of the thumb tangential force was 1.5 times the moment arm of the virtual finger (VF, an imagined finger with the mechanical action equal to that of the four fingers) force. Unlike the situation when there are equal moment arms, the experimental setup forced the subjects to choose between (a) sharing equally the increase in load force between the thumb and VF but generating a moment of tangential force, which had to be compensated by negatively co-varying the moment due to normal forces, or (b) sharing unequally the load force increase between the thumb and VF but preventing generation of a moment of tangential forces. We found that different subjects tended to use one of these two strategies. These findings suggest that the selection by the CNS of prehension synergies at the VF-thumb level with respect to the moment of force is non-obligatory and reflects individual subject preferences. This unequal sharing of the load by the tangential forces, in contrast to the previously observed equal sharing, suggests that the invariant feature of prehension may be a correlated increase in tangential forces rather than an equal increase.
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Friedman, J., & Flash, T. (2009). Trajectory of the index finger during grasping. Exp Brain Res, 196(4), 497–509.
Abstract: The trajectory of the index finger during grasping movements was compared to the trajectories predicted by three optimization-based models. The three models consisted of minimizing the integral of the weighted squared joint derivatives along the path (inertia-like cost), minimizing torque change, and minimizing angular jerk. Of the three models, it was observed that the path of the fingertip and the joint trajectories, were best described by the minimum angular jerk model. This model, which does not take into account the dynamics of the finger, performed equally well when the inertia of the finger was altered by adding a 20 g weight to the medial phalange. Thus, for the finger, it appears that trajectories are planned based primarily on kinematic considerations at a joint level.
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