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Sonification as a possible stroke rehabilitation strategy.

Scholz DS, Wu L, Pirzer J, Schneider J, Rollnik JD, Großbach M, Altenmüller EO - Front Neurosci (2014)

Bottom Line: In order to examine and validate the effectiveness of sonification in stroke rehabilitation, we developed a computer program, termed "SonicPointer": Participants' computer mouse movements were sonified in real-time with complex tones.Furthermore, the learning curves were steepest when pitch was mapped onto the vertical and brightness onto the horizontal axis.This seems to be the optimal constellation for this two-dimensional sonification.

View Article: PubMed Central - PubMed

Affiliation: Institute of Music Physiology and Musicians' Medicine, University of Music, Drama and Media Hannover, Germany.

ABSTRACT
Despite cerebral stroke being one of the main causes of acquired impairments of motor skills worldwide, well-established therapies to improve motor functions are sparse. Recently, attempts have been made to improve gross motor rehabilitation by mapping patient movements to sound, termed sonification. Sonification provides additional sensory input, supplementing impaired proprioception. However, to date no established sonification-supported rehabilitation protocol strategy exists. In order to examine and validate the effectiveness of sonification in stroke rehabilitation, we developed a computer program, termed "SonicPointer": Participants' computer mouse movements were sonified in real-time with complex tones. Tone characteristics were derived from an invisible parameter mapping, overlaid on the computer screen. The parameters were: tone pitch and tone brightness. One parameter varied along the x, the other along the y axis. The order of parameter assignment to axes was balanced in two blocks between subjects so that each participant performed under both conditions. Subjects were naive to the overlaid parameter mappings and its change between blocks. In each trial a target tone was presented and subjects were instructed to indicate its origin with respect to the overlaid parameter mappings on the screen as quickly and accurately as possible with a mouse click. Twenty-six elderly healthy participants were tested. Required time and two-dimensional accuracy were recorded. Trial duration times and learning curves were derived. We hypothesized that subjects performed in one of the two parameter-to-axis-mappings better, indicating the most natural sonification. Generally, subjects' localizing performance was better on the pitch axis as compared to the brightness axis. Furthermore, the learning curves were steepest when pitch was mapped onto the vertical and brightness onto the horizontal axis. This seems to be the optimal constellation for this two-dimensional sonification.

No MeSH data available.


Related in: MedlinePlus

Learning curves for condition 2. The y axis displays the mean city-block distance between participants' clicks and the target for x and y position of the mouse. The 50 trials were binned into 5 bins of 10 trials as shown on the x axis. The error bars display the lower boundary of a 99 % confidence interval below participants mean click-to-target distance in the corresponding trial bin. Participants showed a significant decrease of click-to-target distance over time for the dimension pitch (red, dashed) but not for brightness (green, solid). **p < 0.01.
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Figure 6: Learning curves for condition 2. The y axis displays the mean city-block distance between participants' clicks and the target for x and y position of the mouse. The 50 trials were binned into 5 bins of 10 trials as shown on the x axis. The error bars display the lower boundary of a 99 % confidence interval below participants mean click-to-target distance in the corresponding trial bin. Participants showed a significant decrease of click-to-target distance over time for the dimension pitch (red, dashed) but not for brightness (green, solid). **p < 0.01.

Mentions: For condition 2 the sound parameter grid was rotated, mapping brightness onto the y axis and pitch onto the x axis. Participants showed a significant learning effect for the parameter pitch displayed by a significant reduction of click-to-target distance over time [χ2(4) = 21.52, p < 0.001] (V = 182.5, p = 0.002) (Figure 6). They did not show a significant reduction of click-to-target distance over time for the parameter brightness [χ2(4) = 7.15, p = 0.128]. Also in condition 2 the click-to-target distances of the participants for brightness were always higher than for pitch. Participants were always further away from the goal for brightness than for pitch. So in condition 2 pitch was again the more effective mapping as displayed in Figure 6 and by the results of the paired Wilcoxon signed-rank test (V = 7896.5, p < 0.001).


Sonification as a possible stroke rehabilitation strategy.

Scholz DS, Wu L, Pirzer J, Schneider J, Rollnik JD, Großbach M, Altenmüller EO - Front Neurosci (2014)

Learning curves for condition 2. The y axis displays the mean city-block distance between participants' clicks and the target for x and y position of the mouse. The 50 trials were binned into 5 bins of 10 trials as shown on the x axis. The error bars display the lower boundary of a 99 % confidence interval below participants mean click-to-target distance in the corresponding trial bin. Participants showed a significant decrease of click-to-target distance over time for the dimension pitch (red, dashed) but not for brightness (green, solid). **p < 0.01.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4202805&req=5

Figure 6: Learning curves for condition 2. The y axis displays the mean city-block distance between participants' clicks and the target for x and y position of the mouse. The 50 trials were binned into 5 bins of 10 trials as shown on the x axis. The error bars display the lower boundary of a 99 % confidence interval below participants mean click-to-target distance in the corresponding trial bin. Participants showed a significant decrease of click-to-target distance over time for the dimension pitch (red, dashed) but not for brightness (green, solid). **p < 0.01.
Mentions: For condition 2 the sound parameter grid was rotated, mapping brightness onto the y axis and pitch onto the x axis. Participants showed a significant learning effect for the parameter pitch displayed by a significant reduction of click-to-target distance over time [χ2(4) = 21.52, p < 0.001] (V = 182.5, p = 0.002) (Figure 6). They did not show a significant reduction of click-to-target distance over time for the parameter brightness [χ2(4) = 7.15, p = 0.128]. Also in condition 2 the click-to-target distances of the participants for brightness were always higher than for pitch. Participants were always further away from the goal for brightness than for pitch. So in condition 2 pitch was again the more effective mapping as displayed in Figure 6 and by the results of the paired Wilcoxon signed-rank test (V = 7896.5, p < 0.001).

Bottom Line: In order to examine and validate the effectiveness of sonification in stroke rehabilitation, we developed a computer program, termed "SonicPointer": Participants' computer mouse movements were sonified in real-time with complex tones.Furthermore, the learning curves were steepest when pitch was mapped onto the vertical and brightness onto the horizontal axis.This seems to be the optimal constellation for this two-dimensional sonification.

View Article: PubMed Central - PubMed

Affiliation: Institute of Music Physiology and Musicians' Medicine, University of Music, Drama and Media Hannover, Germany.

ABSTRACT
Despite cerebral stroke being one of the main causes of acquired impairments of motor skills worldwide, well-established therapies to improve motor functions are sparse. Recently, attempts have been made to improve gross motor rehabilitation by mapping patient movements to sound, termed sonification. Sonification provides additional sensory input, supplementing impaired proprioception. However, to date no established sonification-supported rehabilitation protocol strategy exists. In order to examine and validate the effectiveness of sonification in stroke rehabilitation, we developed a computer program, termed "SonicPointer": Participants' computer mouse movements were sonified in real-time with complex tones. Tone characteristics were derived from an invisible parameter mapping, overlaid on the computer screen. The parameters were: tone pitch and tone brightness. One parameter varied along the x, the other along the y axis. The order of parameter assignment to axes was balanced in two blocks between subjects so that each participant performed under both conditions. Subjects were naive to the overlaid parameter mappings and its change between blocks. In each trial a target tone was presented and subjects were instructed to indicate its origin with respect to the overlaid parameter mappings on the screen as quickly and accurately as possible with a mouse click. Twenty-six elderly healthy participants were tested. Required time and two-dimensional accuracy were recorded. Trial duration times and learning curves were derived. We hypothesized that subjects performed in one of the two parameter-to-axis-mappings better, indicating the most natural sonification. Generally, subjects' localizing performance was better on the pitch axis as compared to the brightness axis. Furthermore, the learning curves were steepest when pitch was mapped onto the vertical and brightness onto the horizontal axis. This seems to be the optimal constellation for this two-dimensional sonification.

No MeSH data available.


Related in: MedlinePlus