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Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke.

Secoli R, Milot MH, Rosati G, Reinkensmeyer DJ - J Neuroeng Rehabil (2011)

Bottom Line: With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully.These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomechatronic Lab,, Department of Mechanical Engineering, University of California, 4200 Engineering Gateway, Irvine, CA 92697-3875, USA. rsecoli@uci.edu

ABSTRACT

Background: Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task.

Methods: Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error.

Results: Participants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.

Conclusions: Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted. Providing an alternate sensory channel for feedback, i.e., auditory feedback of tracking error, enabled the participants to simultaneously perform the tracking task and distracter task effectively. Thus, incorporating real-time auditory feedback of performance errors might improve clinical outcomes of robotic therapy systems.

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Pneu-WREX. Pneumatic exoskeleton [28] used to perform clinical trials.
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Figure 2: Pneu-WREX. Pneumatic exoskeleton [28] used to perform clinical trials.

Mentions: The robot used to assist in performing the tracking task was a pneumatic exoskeleton, the Pneu-WREX [28], which has been used previously in a study of robotic therapy with over 30 participants with chronic stroke [29]. The Pneu-WREX (see Figure 2) evolved from a passive rehabilitation device called the T-WREX [30]. The Pneu-WREX is able to generate large forces within a good dynamic range (like a therapist's assistance) using nonlinear control techniques [31]. The controller used to assist the patient in moving during the experiments was an adaptive controller with a forgetting term developed previously [32]. The adaptive controller uses a measurement of tracking error to build a model of the forces needed to assist the arm in moving. The model is represented as a function of the position of the arm, using radial basis functions whose parameters are updated with a standard adaptive control law; other ways to implement the model have been developed [33]. Building a model of the forces needed to move the arm allows the robot to be made more compliant, since it no longer needs to rely solely on position feedback to decrease tracking error. Essentially, the resulting controller models the forces needed to assist the subject, as learned from tracking errors, and reduces its effort with time on an exponential basis when kinematic error is small.


Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke.

Secoli R, Milot MH, Rosati G, Reinkensmeyer DJ - J Neuroeng Rehabil (2011)

Pneu-WREX. Pneumatic exoskeleton [28] used to perform clinical trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3104373&req=5

Figure 2: Pneu-WREX. Pneumatic exoskeleton [28] used to perform clinical trials.
Mentions: The robot used to assist in performing the tracking task was a pneumatic exoskeleton, the Pneu-WREX [28], which has been used previously in a study of robotic therapy with over 30 participants with chronic stroke [29]. The Pneu-WREX (see Figure 2) evolved from a passive rehabilitation device called the T-WREX [30]. The Pneu-WREX is able to generate large forces within a good dynamic range (like a therapist's assistance) using nonlinear control techniques [31]. The controller used to assist the patient in moving during the experiments was an adaptive controller with a forgetting term developed previously [32]. The adaptive controller uses a measurement of tracking error to build a model of the forces needed to assist the arm in moving. The model is represented as a function of the position of the arm, using radial basis functions whose parameters are updated with a standard adaptive control law; other ways to implement the model have been developed [33]. Building a model of the forces needed to move the arm allows the robot to be made more compliant, since it no longer needs to rely solely on position feedback to decrease tracking error. Essentially, the resulting controller models the forces needed to assist the subject, as learned from tracking errors, and reduces its effort with time on an exponential basis when kinematic error is small.

Bottom Line: With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully.These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biomechatronic Lab,, Department of Mechanical Engineering, University of California, 4200 Engineering Gateway, Irvine, CA 92697-3875, USA. rsecoli@uci.edu

ABSTRACT

Background: Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task.

Methods: Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error.

Results: Participants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.

Conclusions: Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted. Providing an alternate sensory channel for feedback, i.e., auditory feedback of tracking error, enabled the participants to simultaneously perform the tracking task and distracter task effectively. Thus, incorporating real-time auditory feedback of performance errors might improve clinical outcomes of robotic therapy systems.

Show MeSH
Related in: MedlinePlus