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Biess, A., Liebermann, D. G., & Flash, T. (2007). A computational model for redundant human three-dimensional pointing movements: integration of independent spatial and temporal motor plans simplifies movement dynamics. J Neurosci, 27(48), 13045–13064.
Abstract: Few computational models have addressed the spatiotemporal features of unconstrained three-dimensional (3D) arm motion. Empirical observations made on hand paths, speed profiles, and arm postures during point-to-point movements led to the assumption that hand path and arm posture are independent of movement speed, suggesting that the geometric and temporal properties of movements are decoupled. In this study, we present a computational model of 3D movements for an arm with four degrees of freedom based on the assumption that optimization principles are separately applied at the geometric and temporal levels of control. Geometric properties (path and posture) are defined in terms of geodesic paths with respect to the kinetic energy metric in the Riemannian configuration space. Accordingly, a geodesic path can be generated with less muscular effort than on any other, nongeodesic path, because the sum of all configuration-speed-dependent torques vanishes. The temporal properties of the movement (speed) are determined in task space by minimizing the squared jerk along the selected end-effector path. The integration of both planning levels into a single spatiotemporal representation simplifies the control of arm dynamics along geodesic paths and results in movements with near minimal torque change and minimal peak value of kinetic energy. Thus, the application of Riemannian geometry allows for a reconciliation of computational models previously proposed for the description of arm movements. We suggest that geodesics are an emergent property of the motor system through the exploration of dynamical space. Our data validated the predictions for joint trajectories, hand paths, final postures, speed profiles, and driving torques.
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Merdler, T., Liebermann, D. G., Levin, M. F., & Berman, S. (2013). Arm-plane representation of shoulder compensation during pointing movements in patients with stroke. J Electromyogr Kinesiol, 23(4), 938–947.
Abstract: Improvements in functional motor activities are often accompanied by motor compensations to overcome persistent motor impairment in the upper limb. Kinematic analysis is used to objectively quantify movement patterns including common motor compensations such as excessive trunk displacement during reaching. However, a common motor compensation to assist reaching, shoulder abduction, is not adequately characterized by current motion analysis approaches. We apply the arm-plane representation that accounts for the co-variation between movements of the whole arm, and investigate its ability to identify and quantify compensatory arm movements in stroke subjects when making forward arm reaches. This method has not been previously applied to the analysis of motion deficits. Sixteen adults with right post-stroke hemiparesis and eight healthy age-matched controls reached in three target directions (14 trials/target; sampling rate: 100Hz). Arm-plane movement was validated against endpoint, joint, and trunk kinematics and compared between groups. In stroke subjects, arm-plane measures were correlated with arm impairment (Fugl-Meyer Assessment) and ability (Box and Blocks) scores and were more sensitive than clinical measures to detect mild motor impairment. Arm-plane motion analysis provides new information about motor compensations involving the co-variation of shoulder and elbow movements that may help to understand the underlying motor deficits in patients with stroke.
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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., Amiaz, A., & Korman, M. (2022). The online and offline effects of changing movement timing variability during training on a finger-opposition task. Sci Rep, 12(1), 13319.
Abstract: In motor learning tasks, there is mixed evidence for whether increased task-relevant variability in early learning stages leads to improved outcomes. One problem is that there may be a connection between skill level and motor variability, such that participants who initially have more variability may also perform worse on the task, so will have more room to improve. To avoid this confound, we experimentally manipulated the amount of movement timing variability (MTV) during training to test whether it improves performance. Based on previous studies showing that most of the improvement in finger-opposition tasks comes from optimizing the relative onset time of the finger movements, we used auditory cues (beeps) to guide the onset times of sequential movements during a training session, and then assessed motor performance after the intervention. Participants were assigned to three groups that either: (a) followed a prescribed random rhythm for their finger touches (Variable MTV), (b) followed a fixed rhythm (Fixed control MTV), or (c) produced the entire sequence following a single beep (Unsupervised control MTV). While the intervention was successful in increasing MTV during training for the Variable group, it did not lead to improved outcomes post-training compared to either control group, and the use of fixed timing led to significantly worse performance compared to the Unsupervised control group. These results suggest that manipulating MTV through auditory cues does not produce greater learning than unconstrained training in motor sequence tasks.
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Dario G. Liebermann, Larry Katz, & and Ruth Morey Sorrentino. (2005). Experienced Coaches’ Attitudes Towards Science and Technology. International Journal of Computer Science in Sport, 4(1), 21–28.
Abstract: In this study, the attitude of experienced coaches towards technologies and sport
sciences was assessed. A questionnaire was used to evaluate three areas: (1)
Attitudes towards technology and sport science in coaching, (2) Technology and
scientific knowledge in practice, and (3) Perceived importance of technology and
science in enhancing sport results. A group of 27 highly experienced coaches
completed the questionnaire. The questionnaire consisted of three parts, starting
with demographic information, followed by a series of 27 questions with answers
on a Likert scale ranging from strongly agree to strongly disagree, and finally,
coaches were requested to rank 14 well-defined ‘coaching goals’ from 1 (most
important) to 14 (least important). Results showed that top-level coaches rated
having a good relationship with the athletes’ as a major goal. Overall, members of
this group of experienced coaches seem to recognize the general importance of
sport sciences, and appear to be positive about the use of sport technologies, but
do not necessarily translate these positive attitudes into actual practice within
their competitive sport environments, even when they all use information
technology for other activities. According to these results, sport science
researchers and technology developers need to adapt their strategies. Coaching
education should encourage coaches to incorporate technologies as part of their
coaching routines. Developing innovative resources and incorporating them in
coaching education, as is done in some countries, may be a starting point.
However, placing the emphasis on educating successful coaches on the practical
use of technology and scientific knowledge is suggested as a short-term goal.
This may allow for a more immediate effect on the attitude and practice of less
senior coaches that tend to adopt methods and training routines through following
the personal example provided by top-level coaches.
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