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Davidowitz, I., Parmet, Y., Frenkel-Toledo, S., Banina, M. C., Soroker, N., Solomon, J. M., et al. (2019). Relationship Between Spasticity and Upper-Limb Movement Disorders in Individuals With Subacute Stroke Using Stochastic Spatiotemporal Modeling. Neurorehabil Neural Repair, 33(2), 141–152.
Abstract: BACKGROUND: Spasticity is common in patients with stroke, yet current quantification methods are insufficient for determining the relationship between spasticity and voluntary movement deficits. This is partly a result of the effects of spasticity on spatiotemporal characteristics of movement and the variability of voluntary movement. These can be captured by Gaussian mixture models (GMMs). OBJECTIVES: To determine the influence of spasticity on upper-limb voluntary motion, as assessed by the bidirectional Kullback-Liebler divergence (BKLD) between motion GMMs. METHODS: A total of 16 individuals with subacute stroke and 13 healthy aged-equivalent controls reached to grasp 4 targets (near-center, contralateral, far-center, and ipsilateral). Two-dimensional GMMs (angle and time) were estimated for elbow extension motion. BKLD was computed for each individual and target, within the control group and between the control and stroke groups. Movement time, final elbow angle, average elbow velocity, and velocity smoothness were computed. RESULTS: Between-group BKLDs were much larger than within control-group BKLDs. Between-group BKLDs for the near-center target were lower than those for the far-center and contralateral targets, but similar to that for the ipsilateral target. For those with stroke, the final angle was lower for the near-center target, and the average velocity was higher. Velocity smoothness was lower for the near-center than for the ipsilateral target. Elbow flexor and extensor passive muscle resistance (Modified Ashworth Scale) strongly explained BKLD values. CONCLUSIONS: Results support the view that individuals with poststroke spasticity have a velocity-dependent reduction in active elbow joint range and that BKLD can be used as an objective measure of the effects of spasticity on reaching kinematics.
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Thorpe, A., Friedman, J., Evans, S., Nesbitt, K., & Eidels, A. (2022). Mouse Movement Trajectories as an Indicator of Cognitive Workload. International Journal of Human-Computer Interaction, 38(15), 1464–1479.
Abstract: Assessing the cognitive impact of user interfaces is a shared focus of human-computer interaction researchers and cognitive scientists. Methods of cognitive assessment based on data derived from the system itself, rather than external apparatus, have the potential to be applied in a range of scenarios. The current study applied methods of analyzing kinematics to mouse movements in a computer-based task, alongside the detection response task, a standard workload measure. Sixty-five participants completed a task in which stationary stimuli were tar;geted using a mouse, with a within-subjects factor of task workload based on the number of targets to be hovered over with the mouse (one/two), and a between-subjects factor based on whether both targets (exhaustive) or just one target (minimum-time) needed to be hovered over to complete a trial when two targets were presented. Mouse movement onset times were slower and mouse movement trajectories exhibited more submovements when two targets were presented, than when one target was presented. Responses to the detection response task were also slower in this condition, indicating higher cognitive workload. However, these differences were only found for participants in the exhaustive condition, suggesting those in the minimum-time condition were not affected by the presence of the second target. Mouse movement trajectory results agreed with other measures of workload and task performance. Our findings suggest this analysis can be applied to workload assessments in real-world scenarios.
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Awasthi, B., Friedman, J., & Williams, M. (2011). Faster, stronger, lateralized: Low spatial frequency information supports face processing. Neuropsychologia, 49(13), 3583–3590.
Abstract: Distinct visual pathways are selectively tuned for processing specific spatial frequencies. Recently, Awasthi, Friedman and Williams (2011) reported fast categorisation of faces at periphery, arguing for primacy of low spatial frequency (LSF) information in face processing. However, previous studies have also documented rapid categorization of places and natural scenes. Here, we tested if the LSF advantage is face specific or also involved in place perception. We used visually guided reaching as a continuous behavioral measure to examine the processing of LSF and high spatial frequency (HSF) hybrids, presented at the periphery. Subjects reached out and touched targets and their movements were recorded. The trajectories revealed that LSF interference was both 95 ms earlier and stronger for faces than places and was lateralized to the left visual field. The early processing of LSF information supports the assumption that faces are prioritised and provides a (neural) framework for such specialised processing.
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Friedman, J., & Korman, M. (2012). Kinematic Strategies Underlying Improvement in the Acquisition of a Sequential Finger Task with Self-Generated vs. Cued Repetition Training. PLoS One, 7(12), e52063.
Abstract: Many motor skills, such as typing, consist of articulating simple movements into novel sequences that are executed faster and smoother with practice. Dynamics of re-organization of these movement sequences with multi-session training and its dependence on the amount of self-regulation of pace during training is not yet fully understood. In this study, participants practiced a sequence of key presses. Training sessions consisted of either externally (Cued) or self-initiated (Uncued) training. Long-term improvements in performance speed were mainly due to reducing gaps between finger movements in both groups, but Uncued training induced higher gains. The underlying kinematic strategies producing these changes and the representation of the trained sequence differed significantly across subjects, although net gains in speed were similar. The differences in long-term memory due to the type of training and the variation in strategies between subjects, suggest that the different neural mechanisms may subserve the improvements observed in overall performance.
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Shaklai, S., Mimouni-Bloch, A., Levin, M., & Friedman, J. (2017). Development of finger force coordination in children. Experimental Brain Research, 235(12), 3709–3720.
Abstract: Coordination is often observed as body parts moving together. However, when producing force with multiple fingers, the optimal coordination is not to produce similar forces with each finger, but rather for each finger to correct mistakes of other fingers. In this study, we aim to determine whether and how this skill develops in children aged 4-12 years. We measured this sort of coordination using the uncontrolled manifold hypothesis (UCM). We recorded finger forces produced by 60 typically developing children aged between 4 and 12 years in a finger-pressing task. The children controlled the height of an object on a screen by the total amount of force they produced on force sensors. We found that the synergy index, a measure of the relationship between “good” and “bad” variance, increased linearly as a function of age. This improvement was achieved by a selective reduction in “bad” variance rather than an increase in “good” variance. We did not observe differences between males and females, and the synergy index was not able to predict outcomes of upper limb behavioral tests after controlling for age. As children develop between the ages of 4 and 12 years, their ability to produce negative covariation between their finger forces improves, likely related to their improved ability to perform dexterous tasks.
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