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Design and preliminary evaluation of the FINGER rehabilitation robot: controlling challenge and quantifying finger individuation during musical computer game play.

Taheri H, Rowe JB, Gardner D, Chan V, Gray K, Bower C, Reinkensmeyer DJ, Wolbrecht ET - J Neuroeng Rehabil (2014)

Bottom Line: The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths.We also used FINGER to measure subjects' effort and finger individuation while playing the game.Test results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mechanical Engineering Department, University of Idaho, Moscow, ID, USA. htaheri@uidaho.edu.

ABSTRACT

Background: This paper describes the design and preliminary testing of FINGER (Finger Individuating Grasp Exercise Robot), a device for assisting in finger rehabilitation after neurologic injury. We developed FINGER to assist stroke patients in moving their fingers individually in a naturalistic curling motion while playing a game similar to Guitar Hero. The goal was to make FINGER capable of assisting with motions where precise timing is important.

Methods: FINGER consists of a pair of stacked single degree-of-freedom 8-bar mechanisms, one for the index and one for the middle finger. Each 8-bar mechanism was designed to control the angle and position of the proximal phalanx and the position of the middle phalanx. Target positions for the mechanism optimization were determined from trajectory data collected from 7 healthy subjects using color-based motion capture. The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths. For initial evaluation, we asked individuals with a stroke (n = 16) and without impairment (n = 4) to play a game similar to Guitar Hero while connected to FINGER.

Results: Precision design, low friction bearings, and separate high speed linear actuators allowed FINGER to individually actuate the fingers with a high bandwidth of control (-3 dB at approximately 8 Hz). During the tests, we were able to modulate the subject's success rate at the game by automatically adjusting the controller gains of FINGER. We also used FINGER to measure subjects' effort and finger individuation while playing the game.

Conclusions: Test results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

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Related in: MedlinePlus

Actual success rates of stroke and unimpaired subjects. Actual success rates of stroke (top) and unimpaired (bottom) subjects for songs with desired success rates of 50% (red), 75% (green), and 99% (blue). Plots to the left show time progression of success rates. Lines are the moving window average over subjects and the shaded area is the standard deviation. Plots to the right show mean and standard deviation of desired vs. actual success rates at convergence.
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Figure 13: Actual success rates of stroke and unimpaired subjects. Actual success rates of stroke (top) and unimpaired (bottom) subjects for songs with desired success rates of 50% (red), 75% (green), and 99% (blue). Plots to the left show time progression of success rates. Lines are the moving window average over subjects and the shaded area is the standard deviation. Plots to the right show mean and standard deviation of desired vs. actual success rates at convergence.

Mentions: Average probability of success in hitting correct notes during gameplay versus time for the sixteen impaired and the four healthy subjects is shown in Figure 13. At the desired success rates of 50%, 75% and 99% the impaired subjects converged to the average actual success rates of 47.7+/−9.6%, 73.8+/−7.1%, and 97.6+/−1.9%. However, the unimpaired subjects converged to the average actual success rates of 72.2+/−19.5%, 79.3+/−4%, and 99+/−1.1%. This result shows that the algorithm explained in 4.3 is successful in assisting subjects to achieve a desired success rate. It is not surprising that the healthy subjects could achieve success rates higher than algorithm’s desired success rate, because the algorithm doesn’t prevent subjects from hitting more correct notes than desired. In order to effectively challenge the unimpaired subject, the algorithm would need to have been able to make the game more difficult than it would naturally be with the assistance turned completely off. This is not necessary for the impaired subjects, whose reduced neuromuscular ability provided the increased difficulty.


Design and preliminary evaluation of the FINGER rehabilitation robot: controlling challenge and quantifying finger individuation during musical computer game play.

Taheri H, Rowe JB, Gardner D, Chan V, Gray K, Bower C, Reinkensmeyer DJ, Wolbrecht ET - J Neuroeng Rehabil (2014)

Actual success rates of stroke and unimpaired subjects. Actual success rates of stroke (top) and unimpaired (bottom) subjects for songs with desired success rates of 50% (red), 75% (green), and 99% (blue). Plots to the left show time progression of success rates. Lines are the moving window average over subjects and the shaded area is the standard deviation. Plots to the right show mean and standard deviation of desired vs. actual success rates at convergence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928667&req=5

Figure 13: Actual success rates of stroke and unimpaired subjects. Actual success rates of stroke (top) and unimpaired (bottom) subjects for songs with desired success rates of 50% (red), 75% (green), and 99% (blue). Plots to the left show time progression of success rates. Lines are the moving window average over subjects and the shaded area is the standard deviation. Plots to the right show mean and standard deviation of desired vs. actual success rates at convergence.
Mentions: Average probability of success in hitting correct notes during gameplay versus time for the sixteen impaired and the four healthy subjects is shown in Figure 13. At the desired success rates of 50%, 75% and 99% the impaired subjects converged to the average actual success rates of 47.7+/−9.6%, 73.8+/−7.1%, and 97.6+/−1.9%. However, the unimpaired subjects converged to the average actual success rates of 72.2+/−19.5%, 79.3+/−4%, and 99+/−1.1%. This result shows that the algorithm explained in 4.3 is successful in assisting subjects to achieve a desired success rate. It is not surprising that the healthy subjects could achieve success rates higher than algorithm’s desired success rate, because the algorithm doesn’t prevent subjects from hitting more correct notes than desired. In order to effectively challenge the unimpaired subject, the algorithm would need to have been able to make the game more difficult than it would naturally be with the assistance turned completely off. This is not necessary for the impaired subjects, whose reduced neuromuscular ability provided the increased difficulty.

Bottom Line: The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths.We also used FINGER to measure subjects' effort and finger individuation while playing the game.Test results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mechanical Engineering Department, University of Idaho, Moscow, ID, USA. htaheri@uidaho.edu.

ABSTRACT

Background: This paper describes the design and preliminary testing of FINGER (Finger Individuating Grasp Exercise Robot), a device for assisting in finger rehabilitation after neurologic injury. We developed FINGER to assist stroke patients in moving their fingers individually in a naturalistic curling motion while playing a game similar to Guitar Hero. The goal was to make FINGER capable of assisting with motions where precise timing is important.

Methods: FINGER consists of a pair of stacked single degree-of-freedom 8-bar mechanisms, one for the index and one for the middle finger. Each 8-bar mechanism was designed to control the angle and position of the proximal phalanx and the position of the middle phalanx. Target positions for the mechanism optimization were determined from trajectory data collected from 7 healthy subjects using color-based motion capture. The resulting robotic device was built to accommodate multiple finger sizes and finger-to-finger widths. For initial evaluation, we asked individuals with a stroke (n = 16) and without impairment (n = 4) to play a game similar to Guitar Hero while connected to FINGER.

Results: Precision design, low friction bearings, and separate high speed linear actuators allowed FINGER to individually actuate the fingers with a high bandwidth of control (-3 dB at approximately 8 Hz). During the tests, we were able to modulate the subject's success rate at the game by automatically adjusting the controller gains of FINGER. We also used FINGER to measure subjects' effort and finger individuation while playing the game.

Conclusions: Test results demonstrate the ability of FINGER to motivate subjects with an engaging game environment that challenges individuated control of the fingers, automatically control assistance levels, and quantify finger individuation after stroke.

Show MeSH
Related in: MedlinePlus