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Banina, M. C., Molad, R., Solomon, J. S., Berman, S., Soroker, N., Frenkel-Toledo, S., et al. (2020). Exercise intensity of the upper limb can be enhanced using a virtual rehabilitation system. Disabil Rehabil Assist Technol, , 1–7.
Abstract: Purpose: Motor recovery of the upper limb (UL) is related to exercise intensity, defined as movement repetitions divided by minutes in active therapy, and task difficulty. However, the degree to which UL training in virtual reality (VR) applications deliver intense and challenging exercise and whether these factors are considered in different centres for people with different sensorimotor impairment levels is not evidenced. We determined if (1) a VR programme can deliver high UL exercise intensity in people with sub-acute stroke across different environments and (2) exercise intensity and difficulty differed among patients with different levels of UL sensorimotor impairment.Methods: Participants with sub-acute stroke (<6 months) with Fugl-Meyer scores ranging from 14 to 57, completed 10 approximately 50-min UL training sessions using three unilateral and one bilateral VR activity over 2 weeks in centres located in three countries. Training time, number of movement repetitions, and success rates were extracted from game activity logs. Exercise intensity was calculated for each participant, related to UL impairment, and compared between centres.Results: Exercise intensity was high and was progressed similarly in all centres. Participants had most difficulty with bilateral and lateral reaching activities. Exercise intensity was not, while success rate of only one unilateral activity was related to UL severity.Conclusion: The level of intensity attained with this VR exercise programme was higher than that reported in current stroke therapy practice. Although progression through different activity levels was similar between centres, clearer guidelines for exercise progression should be provided by the VR application.Implications for rehabilitationVR rehabilitation systems can be used to deliver intensive exercise programmes.VR rehabilitation systems need to be designed with measurable progressions through difficulty levels.
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Liebermann, D. G., & Goodman, D. (1991). Effects of visual guidance on the reduction of impacts during landings. Ergonomics, 34(11), 1399–1406.
Abstract: While a common view is that vision is essential to motor performance, some recent studies have shown that continuous visual guidance may not always be required within certain time constraints. This study investigated a landing-related task (self-released falls) to assess the extent to which visual information enhances the ability to reduce the impacts at touchdown. Six individuals performed six blocked trials from four height categories in semi-counterbalanced order (5-10, 20-25, 60-65, and 90-95 cm) in vision and no-vision conditions randomly assigned. A series of two-way ANOVA with repeated measures were carried out separately on each dependent variable collapsed over six trials. The results indicated that vision during the flight did not produce softer landings. Indeed, in analysing the first peak (PFP) a main effect for visual condition was revealed in that the mean amplitude was slightly higher when vision was available (F(1,5) = 6.57; p less than 0.05), thus implicating higher forces at impact. The results obtained when the time to the first peak (TFP) was applied showed no significant differences between conditions (F(1,5) less than 1). As expected, in all cases, the analyses yielded significant main effects for the height categories factor. It appears that during self-initiated falls in which the environmental cues are known before the event, visual guidance is not necessary in order to adopt a softer landing strategy.
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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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