• Journal of International Academy of Physical Therapy Research
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• v.2 no.2
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• pp.339-343
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• 2011

The purpose of this study is to see the effect of functional electrical stimulation on forced vital capacity and alternating motion rate in children with spastic cerebral palsy. This study divided 20 children with spastic cerebral palsy into two groups; functional electrical stimulation treatment group and control group. Functional electrical stimulation treatment group had 20min per day treatment three times a week for four weeks and the control group did not have any treatment. Before and after intervention, this study measured forced vital capacity and alternate motion rate(/peo/,/teo/) for all children. Forced vital capacity showed statistically significant increase for the group with functional electrical stimulation(p<.05) while the control group did not show any significant increase(p>.05). Alternate motion rate showed statistically significant increase for the group with functional electrical stimulation(p<.05) while the control group did not show any significant increase(p>.05). This result shows that functional electrical stimulation affected the ability of the children with spastic cerebral palsy who have decreased breathing and phonation capability.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.145-152
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• 2024

Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.