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James W. Roberts, James Lyons, Daniel B. L. Garcia, Raquel Burgess, and Digby Elliott

The multiple process model contends that there are two forms of online control for manual aiming: impulse regulation and limb-target control. This study examined the impact of visual information processing for limb-target control. We amalgamated the Gunslinger protocol (i.e., faster movements following a reaction to an external trigger compared with the spontaneous initiation of movement) and Müller-Lyer target configurations into the same aiming protocol. The results showed the Gunslinger effect was isolated at the early portions of the movement (peak acceleration and peak velocity). Reacted aims reached a longer displacement at peak deceleration, but no differences for movement termination. The target configurations manifested terminal biases consistent with the illusion. We suggest the visual information processing demands imposed by reacted aims can be adapted by integrating early feedforward information for limb-target control.

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Matthew Heath, David A. Westwood, and Gordon Binsted

The goal of the present investigation was to explore the putative contributions of feedforward- and feedback-based processes in the control of memory-guided reaching movements. Participants (N = 4) completed an extensive number of reaching movements (2700) to 3 midline targets (20, 30, 40 cm) in 6 visual conditions: full-vision, open-loop, and four memory-guided conditions (0, 200, 400, and 600 ms of delay). To infer limb control, we used a regression technique to examine the within-trial correspondence between the spatial position of the limb at peak acceleration, peak velocity, peak deceleration, and the ultimate movement endpoint. A high degree of within-trial correspondence would suggest that the final position of the limb was largely specified prior to movement onset and not adjusted during the action (i.e., feedforward control); conversely, a low degree of within-trial correspondence would suggest that movements were modified during the reaching trajectory (i.e., feedback control). Full-vision reaches were found to be more accurate and less variable than open-loop and memory-guided reaches. Moreover, full-vision reaches demonstrated only modest within-trial correspondence between the spatial position of the limb at each kinematic marker and the ultimate movement endpoint, suggesting that reaching accuracy was achieved by adjusting the limb trajectory throughout the course of the action. Open-loop and memory-guided movements exhibited strong within-trial correspondence between final limb position and the position of the limb at peak velocity and peak deceleration. This strong correspondence indicates that the final position of the limb was largely determined by processes that occurred before the reach was initiated; errors in the planning process were not corrected during the course of the action. Thus, and contrary to our previous findings in a video-based aiming task, it appears that stored target information is not extensively (if at all) used to modify the trajectory of reaching movements to remembered targets in peripersonal space.

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Arthur D. Kuo

A simple pendulum model is used to study how feedforward and feedback can be combined to control rhythmic limb movements. I show that a purely feedforward central pattern generator (CPG) is highly sensitive to unexpected disturbances. Pure feedback control analogous to reflex pathways can compensate for disturbances but is sensitive to imperfect sensors. I demonstrate that for systems subject to both unexpected disturbances and sensor noise, a combination of feedforward and feedback can improve performance. This combination is achieved by using a state estimation interpretation, in which a neural oscillator acts as an internal model of limb motion that predicts the state of the limb, and by using alpha-gamma coactivation or its equivalent to generate a sensory error signal that is fed back to entrain the neural oscillator. Such a hybrid feedforward/feedback system can optimally compensate for both disturbances and sensor noise, yet it can also produce fictive locomotion when sensory output is removed, as is observed biologically. CPG behavior arises due to the interaction of the internal model and a feedback control that uses the predicted state. I propose an interpretation of the neural oscillator as a filter for processing sensory information rather than as a generator of commands.

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Hooman Minoonejad, Mohammad Karimizadeh Ardakani, Reza Rajabi, Erik A. Wikstrom, and Ali Sharifnezhad

variety of sources, integrate and interpret that data, and select appropriate motor commands to achieve a movement goal. 5 Unfortunately, lateral ankle sprains and CAI result in feedback and feedforward neuromuscular control alterations. 6 Of particular interest are the altered muscle activity levels

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Luiz C. Santos, Renato Moraes, and Aftab E. Patla

The purpose of the current study was to understand how visual information about an ongoing change in obstacle size is used during obstacle avoidance for both lead and trail limbs. Participants were required to walk in a dark room and to step over an obstacle edged with a special tape visible in the dark. The obstacle’s dimensions were manipulated one step before obstacle clearance by increasing or decreasing its size. Two increasing and two decreasing obstacle conditions were combined with seven control static conditions. Results showed that information about the obstacle’s size was acquired and used to modulate trail limb trajectory, but had no effect on lead limb trajectory. The adaptive step was influenced by the time available to acquire and process visual information. In conclusion, visual information about obstacle size acquired during lead limb crossing was used in a feedforward manner to modulate trail limb trajectory.

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Lawrence E. M. Grierson, Claudia Gonzalez, and Digby Elliott

This study was designed to examine the importance of vision to corrective processes associated with a mechanical perturbation to the limb during goal-directed aiming. With a hand held stylus, under vision and no vision conditions, performers reached to a target represented by the intersection of perpendicular lines. The stylus was connected to an air compressor and engineered such that 80 ms following movement initiation reaches were perturbed by a short air burst either in the direction of, or opposite to, the movement. Spatial position analysis of the limb at early kinematic landmarks revealed that the single direction bursts were successful in advancing and hindering the movement progress. Furthermore, within subject trial-to-trial variability analysis indicated that performers adopted different control strategies for dealing with the perturbations depending on the availability of vision. The present findings suggest that a continuous form of online control is exercised during the early portions of the aiming trajectories. This form of control may be mediated by visual or proprioceptive information.

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Matthew Heath and David A. Westwood

We investigated whether a representation of a visual target can be stored in memory and used to support the online control of reaching movements. To distinguish between the use of a stored target representation for movement planning versus online control, we employed a novel movement environment in which participants could not fully plan their action in advance of movement initiation; that is, the spatial mapping between the movement of a computer mouse and the on-screen movement of a cursor was randomly varied from trial to trial. As such, participants were required to use online control to reach the target position. Reaches were examined in full-vision and three memory-dependent conditions (0, 2, and 5 s of delay). Absolute constant error did not accumulate between full-vision and brief delay trials (i.e., the 0-s delay), suggesting a stored representation of the visual target can be used for online control of reaching given a sufficiently brief delay interval. Longer delay trials (2 and 5 s) were less accurate and more variable than brief delay trials; however, the residual accuracy of these memory-dependent actions suggests that the motor system may have access to a stored representation of the visual target for online control processes for upwards of 5 s following target occlusion.

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Swati Shenoy and Alexander S. Aruin

The objective of this study was to determine if a forward-tilted seat and the resultant semi-kneeling body position associated with sitting on the Balans Multi Chair (BMC) affect postural control in sitting. Nine healthy subjects were seated on either the BMC or a regular (REG) chair with their arms extended. They were instructed to induce self-initiated body perturbations in four different directions by exerting brief pulses of force against a stationary frame positioned in front of them. Electromyographic (EMG) activities of trunk and leg muscles were recorded before and during the perturbations. The results show that sitting on both types of chairs was associated with anticipatory activation of trunk and upper leg muscles. In contrast, anticipatory activation of distal muscles was observed while sitting only on the REG chair and was absent while sitting on the BMC. The outcome of the study suggests that although the forward-tilting seat and semi-kneeling body position might help in preserving a normal lordosis, it is not associated with anticipatory activation of lower leg muscles, which might reduce the ability of an individual to counteract self-initiated body perturbations. These findings stress the important role of chair designs in the control of sitting posture.

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Dafne Pires Pinto, Pedro Vieira Sarmet Moreira, and Luciano Luporini Menegaldo

musculoskeletal system, in a mixed feedback and feed-forward control ( Teixeira et al., 2020 ). Rambling (RM) and trembling (TR) decomposition of the center of pressure (COP) trajectory in quiet bipedal standing allow for the assessment of two mechanisms contributing to maintain upright posture ( Zatsiorsky

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Shohei Shibata, Yuki Inaba, Shinsuke Yoshioka, and Senshi Fukashiro

delay. Finger torque can be viewed as a consequence of feed-forward adjustments from the CNS ( Ambike, Paclet, Latash, & Zatsiorsky, 2013 ). From these results, to stabilize release timing, the CNS seems to have synchronized the wrist torque and finger torque by feed-forward adjustments. Conversely