A Study of Postural Control Characteristics in Schoolchild with Intellectual Disability (초등학교 지적장애아동의 자세조절 특성)
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- 재활복지
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- v.14 no.3
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- pp.225-256
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- 2010
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This study aims to provide the basic data of the rehabilitation program for the schoolchild with intellectual disability by designing new framework of the features of postural control for the schoolchild with intellectual disability. For this, the study investigated what sensations the schoolchild are using to maintain posture by selectively or synthetically applying vision, vestibular sensation and somato-sensation, and how the coordinative sensory system of the schoolchild is responding to any sway referenced sensory stimulus. The study intended to prove the limitation of motor system in estimating the postural stability by providing the cognitive motor task, and provided the features of postural control of the schoolchild with intellectual disability by measuring the onset times and orders of muscle contraction of neuron-muscle when there is a postural control taking place due to the exterior disturbance. Furthermore, by comparatively analyzing the difference between the normal schoolchild and the intellectually disabled schoolchild, this study provided an optimal direction for treatment planning when the rehabilitation program is applied in the postural control ability training program for the schoolchild with intellectual disability. Taking gender and age into consideration, 52 schoolchild including 26 normal schoolchild and 26 intellectually disabled schoolchild were selected. To measure the features of postural control, CTSIB test, and postural control strategy test were conducted. The result of experiment is as followed. First, the schoolchild with intellectual disability showed different feature in using sensory system to control posture. The normal schoolchild tended to depend on somato-sensory or vision, and showed a stable postural control toward a sway referenced stimulus on somato-sensory system. The schoolchild with intellectual disability tended to use somato-sensory or vision, and showed a very instable postural control toward a sway referenced vision or a sway referenced stimulus on somato-sensory system. In sensory analysis, the schoolchild with intellectual disability showed lower level of proficiency in somato-sensation percentile, vision percentile and vestibular sensation percentile compare to the normal schoolchild. Second, as for the onset times and orders of muscle contraction for strategies of postural control when there is an exterior physical stimulus, the schoolchild with intellectual disability showed a relatively delayed onset time of muscle control, and it was specially greater when the perturbation is from backward. As for the onset orders of muscle contraction, it started from muscles near coax then moved to the muscles near ankle joint, and the numbers and kinds of muscles involved were greater than the normal schoolchild. The normal schoolchild showed a fast muscle contracting reaction from every direction after the perturbation stimulus, and the contraction started from the muscles near the ankle joint and expanded to the muscles near coax. From the results of the experiments, the special feature of the postural control of the schoolchild with intellectual disability is that they have a higher dependence on vision in sensory system, and there was no appropriate integration of swayed sensation observed in upper level of central nerve system. In the motor system, the onset time of muscle contraction for postural control was delayed, and it proceeded in reversed order of the normal schoolchild. Therefore, when use the clinical physical therapy to improve the postural control ability, various sensations should be provided and should train the schoolchild to efficiently use the provided sensations and use the sensory experience recorded in upper level of central nerve system to improve postural control ability. At the same time, a treatment program that can improve the processing ability of central nerve system through meaningful activities with organizing and planning adapting reaction should be provided. Also, a proprioceptive motor control training program that can induce faster muscle contraction reaction and more efficient onset orders from muscularskeletal system is need to be provided as well.
Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.