• Title/Summary/Keyword: Assistive Technology Evaluation Tool

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Satisfaction Evaluation for Tablet-based Smart AAC Device (태블릿 기반 스마트 AAC 기기 만족도 평가)

  • Kong, Jin-Yong;An, Na-Yeon
    • Journal of Digital Contents Society
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    • v.19 no.2
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    • pp.251-257
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    • 2018
  • The purpose of this study is to analyze the satisfaction and requirements of users' devices after development of tablet based AAC(Augmentative and Alternatice Communication) application for Android. The purpose of this study is to evaluate the satisfaction and the requirement of tablet - based AAC application, Were assessed using the Korea Assistive Technology Assessment Tool (KAAT). As a result of satisfaction evaluation of tablet-based smart AAC device, all the items including device, service, and everyday life showed positive response satisfying from 5 point scale to more than 4 point scale. However, it was relatively low in the items of effectiveness, manipulation and convenience. Some of the improvements in the application include the enlargement of the symbol sound and the simplicity of symbol editing. The results of the study suggest that continual updates of m Smart AAC applications, the simplicity of symbolic editing, application usage and training should be improved. The satisfaction evaluation results of this study and the feedback of potential users will be the guidelines for improving and complementing the functions of existing smart AAC devices.

The Reliability and Validity of Useful Field of View Test (UFOV(Useful Field of View test) 검사의 신뢰도 및 타당도 검증)

  • Kwak, Ho-Soung;Jung, Bong-Keun
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.11 no.2
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    • pp.157-163
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    • 2017
  • The aim of the study is to examine the reliability and validity of UFOV, which is a visual driving evaluation tool that has been proven to be reliable and valid in western countries, for the purpose of adapting the tool in a systematic manner to the South Korean population. Two evaluator assessed 23 healthy and 19 stroke patients with UFOV, Trail Making Test A & B(TMT A & B) and Motor Free Visual Perception Test(MVPT) from 7 October 2014 to 25 November, 2014. The researcher analyzed inter-rater reliability, correlation between raters of UFOV with Intraclass correlation coefficient, test-retest reliablility, UFOV with spearman correlation coefficient, concurrent validity, UFOV, TMT A & B and MVPT with spearman correlation coefficient, and discriminative validity, comparison mean scores of UFOV between groups, healthy and stroke with Mann-Whitney U test. UFOV score of participants with stroke had lower compared to the healthy control group. The inter-rater reliability(p<.001), test-retest reliability(p<.01) and concurrent validity(p<.01) was statistically significant. Also discriminant validity was statistically significant(p<.001). Based on this study, Use of UFOV for drivers at risk is essential to prevent future traffic accidents and support driving rehabilitation.

Berg Balance Scale Score Classification Study Using Inertial Sensor (관성센서를 이용한 버그균형검사 점수 분류 연구)

  • Hong, Sangpyo;Kim, Yeon-wook;Cho, WooHyeong;Joa, Kyung-Lim;Jung, Han-Young;Kim, K.S.;Lee, S.M.
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.11 no.1
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    • pp.53-62
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    • 2017
  • In this paper, we present the score classification accuracy of BBS(Berg Balance Scale) which is the most commonly used balance evaluation tool using machine learning. Data acquisition was performed using the Noraxon system and an inertial sensor of Noraxon system was attached to the body in 8 locations (left and right ankle, left and right upper buttocks, left and right wrists, back, forehead). Based on the 3-axis accelerometer of the inertial sensor, the feature vector STFT(Short Time Fourier Transform) and SAM(Signal Area Magnitude) were extracted. Then, the items of the BBS were divided into static movement and dynamic movement depending on the operation characteristics, and the feature vectors were selected according to the sensor attachment positions which affect the score for each item of the BBS. Feature vectors selected for each item of BBS were classified using GMM(Gaussian Mixture Model). As a result of the accuracy calculation for 40 subjects, 55.5%, 72.2%, 87.5%, 50%, 35.1%, 62.5%, 43.3%, 58.6%, 60.7%, 33.3%, 44.8%, 89.2%, 51.8%, 85.1%, respectively.