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    ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ๋ถ„์„: ์—ฐ์†์ ์ธ ๋ฐœํ™”์˜ ์Œํ–ฅ์  ๋ณ€ํ™”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Major depressive disorder (commonly referred to as depression) is a common disorder that affects 3.8% of the world's population. Depression stems from various causes, such as genetics, aging, social factors, and abnormalities in the neurotransmitter system; thus, early detection and monitoring are essential. The human voice is considered a representative biomarker for observing depression; accordingly, several studies have developed an automatic depression diagnosis system based on speech. However, constructing a speech corpus is a challenge, studies focus on adults under 60 years of age, and there are insufficient medical hypotheses based on the clinical findings of psychiatrists, limiting the evolution of the medical diagnostic tool. Moreover, the effect of taking antipsychotic drugs on speech characteristics during the treatment phase is overlooked. Thus, this thesis studies a speech-based automatic depression diagnosis system at the semantic level (sentence). First, to analyze depression among the elderly whose emotional changes do not adequately reflect speech characteristics, it developed the mood-induced sentence to build the elderly depression speech corpus and designed an automatic depression diagnosis system for the elderly. Second, it constructed an extrapyramidal symptom speech corpus to investigate the extrapyramidal symptoms, a typical side effect that can appear from an antipsychotic drug overdose. Accordingly, there is a strong correlation between the antipsychotic dose and speech characteristics. The study paved the way for a comprehensive examination of the automatic diagnosis system for depression.์ฃผ์š” ์šฐ์šธ ์žฅ์•  ์ฆ‰ ํ”ํžˆ ์šฐ์šธ์ฆ์ด๋ผ๊ณ  ์ผ์ปฌ์–ด์ง€๋Š” ๊ธฐ๋ถ„ ์žฅ์• ๋Š” ์ „ ์„ธ๊ณ„์ธ ์ค‘ 3.8%์— ๋‹ฌํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ๊ฒช์€๋ฐ” ์žˆ๋Š” ๋งค์šฐ ํ”ํ•œ ์งˆ๋ณ‘์ด๋‹ค. ์œ ์ „, ๋…ธํ™”, ์‚ฌํšŒ์  ์š”์ธ, ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ ์ฒด๊ณ„์˜ ์ด์ƒ๋“ฑ ๋‹ค์–‘ํ•œ ์›์ธ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ์šฐ์šธ์ฆ์€ ์กฐ๊ธฐ ๋ฐœ๊ฒฌ ๋ฐ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ๊ด€๋ฆฌ๊ฐ€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ธ๊ฐ„์˜ ์Œ์„ฑ์€ ์šฐ์šธ์ฆ์„ ๊ด€์ฐฐํ•˜๊ธฐ์— ๋Œ€ํ‘œ์ ์ธ ๋ฐ”์ด์˜ค๋งˆ์ปค๋กœ ์—ฌ๊ฒจ์ ธ ์™”์œผ๋ฉฐ, ์Œ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์Œ์„ฑ ๋ง๋ญ‰์น˜ ๊ตฌ์ถ•์˜ ์–ด๋ ค์›€๊ณผ 60์„ธ ์ดํ•˜์˜ ์„ฑ์ธ๋“ค์—๊ฒŒ ์ดˆ์ ์ด ๋งž์ถ”์–ด์ง„ ์—ฐ๊ตฌ, ์ •์‹ ๊ณผ ์˜์‚ฌ๋“ค์˜ ์ž„์ƒ ์†Œ๊ฒฌ์„ ๋ฐ”ํƒ•์œผ๋กœํ•œ ์˜ํ•™์  ๊ฐ€์„ค ์„ค์ •์˜ ๋ฏธํก๋“ฑ์˜ ํ•œ๊ณ„์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์˜๋ฃŒ ์ง„๋‹จ ๊ธฐ๊ตฌ๋กœ ๋ฐœ์ „ํ•˜๋Š”๋ฐ ํ•œ๊ณ„์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ํ•ญ์ •์‹ ์„ฑ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ์ด ์Œ์„ฑ ํŠน์ง•์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ ๋˜ํ•œ ๊ฐ„๊ณผ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„์˜ ํ•œ๊ณ„์ ๋“ค์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•œ ์˜๋ฏธ๋ก ์  ์ˆ˜์ค€ (๋ฌธ์žฅ ๋‹จ์œ„)์—์„œ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์šฐ์„ ์ ์œผ๋กœ ๊ฐ์ •์˜ ๋ณ€ํ™”๊ฐ€ ์Œ์„ฑ ํŠน์ง•์„ ์ž˜ ๋ฐ˜์˜๋˜์ง€ ์•Š๋Š” ๋…ธ์ธ์ธต์˜ ์šฐ์šธ์ฆ ๋ถ„์„์„ ์œ„ํ•ด ๊ฐ์ • ๋ฐœํ™” ๋ฌธ์žฅ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋…ธ์ธ ์šฐ์šธ์ฆ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ๋ฌธ์žฅ ๋‹จ์œ„์—์„œ์˜ ๊ด€์ฐฐ์„ ํ†ตํ•ด ๋…ธ์ธ ์šฐ์šธ์ฆ ๊ตฐ์—์„œ ๊ฐ์ • ๋ฌธ์žฅ ๋ฐœํ™”๊ฐ€ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ฐ์ • ์ „์ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๋…ธ์ธ์ธต์˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๊ณผ๋ณต์šฉ์œผ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ๋ถ€์ž‘์šฉ์ธ ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ๋Ÿ‰๊ณผ ์Œ์„ฑ ํŠน์ง•๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์šฐ์šธ์ฆ์˜ ์น˜๋ฃŒ ๊ณผ์ •์—์„œ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์ด ์Œ์„ฑ์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์˜์—ญ์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Research Motivations 3 1.1.1 Bridging the Gap Between Clinical View and Engineering 3 1.1.2 Limitations of Conventional Depressed Speech Corpora 4 1.1.3 Lack of Studies on Depression Among the Elderly 4 1.1.4 Depression Analysis on Semantic Level 6 1.1.5 How Antipsychotic Drug Affects the Human Voice? 7 1.2 Thesis objectives 9 1.3 Outline of the thesis 10 Chapter 2 Theoretical Background 13 2.1 Clinical View of Major Depressive Disorder 13 2.1.1 Types of Depression 14 2.1.2 Major Causes of Depression 15 2.1.3 Symptoms of Depression 17 2.1.4 Diagnosis of Depression 17 2.2 Objective Diagnostic Markers of Depression 19 2.3 Speech in Mental Disorder 19 2.4 Speech Production and Depression 21 2.5 Automatic Depression Diagnostic System 23 2.5.1 Acoustic Feature Representation 24 2.5.2 Classification / Prediction 27 Chapter 3 Developing Sentences for New Depressed Speech Corpus 31 3.1 Introduction 31 3.2 Building Depressed Speech Corpus 32 3.2.1 Elements of Speech Corpus Production 32 3.2.2 Conventional Depressed Speech Corpora 35 3.2.3 Factors Affecting Depressed Speech Characteristics 39 3.3 Motivations 40 3.3.1 Limitations of Conventional Depressed Speech Corpora 40 3.3.2 Attitude of Subjects to Depression: Masked Depression 43 3.3.3 Emotions in Reading 45 3.3.4 Objectives of this Chapter 45 3.4 Proposed Methods 46 3.4.1 Selection of Words 46 3.4.2 Structure of Sentence 47 3.5 Results 49 3.5.1 Mood-Inducing Sentences (MIS) 49 3.5.2 Neutral Sentences for Extrapyramidal Symptom Analysis 49 3.6 Summary 51 Chapter 4 Screening Depression in The Elderly 52 4.1 Introduction 52 4.2 Korean Elderly Depressive Speech Corpus 55 4.2.1 Participants 55 4.2.2 Recording Procedure 57 4.2.3 Recording Specification 58 4.3 Proposed Methods 59 4.3.1 Voice-based Screening Algorithm for Depression 59 4.3.2 Extraction of Acoustic Features 59 4.3.3 Feature Selection System and Distance Computation 62 4.3.4 Classification and Statistical Analyses 63 4.4 Results 65 4.5 Discussion 69 4.6 Summary 74 Chapter 5 Correlation Analysis of Antipsychotic Dose and Speech Characteristics 75 5.1 Introduction 75 5.2 Korean Extrapyramidal Symptoms Speech Corpus 78 5.2.1 Participants 78 5.2.2 Recording Process 79 5.2.3 Extrapyramidal Symptoms Annotation and Equivalent Dose Calculations 80 5.3 Proposed Methods 81 5.3.1 Acoustic Feature Extraction 81 5.3.2 Speech Characteristics Analysis recording to Eq.dose 83 5.4 Results 83 5.5 Discussion 87 5.6 Summary 90 Chapter 6 Conclusions and Future Work 91 6.1 Conclusions 91 6.2 Future work 95 Bibliography 97 ์ดˆ ๋ก 121๋ฐ•

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    ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ๊ฐ€์†๋„ ๋ฐ ์ž์ด๋กœ ์„ผ์„œ ๋ฐ์ดํ„ฐ ํ™œ์šฉ ๋‚™์ƒ๊ฐ์ง€ ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณตํ•™์ „๋ฌธ๋Œ€ํ•™์› ์‘์šฉ๊ณตํ•™๊ณผ, 2022.2. ์กฐ์„ฑ์ค€.As the world enters a super-aged society, fall accidents of elderly people are significantly increasing. These fall accidents, if not detected in time, may lead to serious consequences such as death in the worst cases. Therefore, when a fall accident occurs, it is necessary to establish a system for immediately detection. Among various methods for detecting falls, a device that is easy to wear and can be applied indoors and outdoors is devised. This study aims to develop a model that measures people movement using wearable-based accelerometer sensors and gyro sensors, analyzes acceleration and angular velocity, and classifies whether a fall occurs. In order to obtain data, an experiment was conducted in which 12 ADL movements and 4 Fall movements were repeatedly performed while the subjects were wearing a wearable device. ADL movements include sitting, standing, and walking, and the Fall movement consisting of falling forward and falling backward. In order to detect falls and non-falling, LSTM model of the Recurrent Neural Network (RNN) is used. The model was advanced through a data preprocessing and fine-tuning method applied to the input value of the LSTM model that determines whether to fall or not. In the experimental environment, the fall detection accuracy of the model is 99.91%, which is intended to determine the validity of fall detection from the perspective of deep learning.์ „ ์„ธ๊ณ„๊ฐ€ ์ดˆ๊ณ ๋ นํ™” ์‚ฌํšŒ๋กœ ์ง„์ž…ํ•จ์— ๋”ฐ๋ผ ๋…ธ์ธ ๋‚™์ƒ ์‚ฌ๊ณ ๊ฐ€ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋‚™์ƒ ์‚ฌ๊ณ ๋Š” ์ œ๋•Œ ๊ฐ์ง€๋˜์ง€ ์•Š์„ ๊ฒฝ์šฐ ์ตœ์•…์˜ ๊ฒฝ์šฐ ์‚ฌ๋ง๊นŒ์ง€ ์ด๋ฅผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋‚™์ƒ์ด ๋ฐœ์ƒํ•  ๊ฒฝ์šฐ ์ฆ‰์‹œ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์ด ์š”๊ตฌ๋œ๋‹ค. ๋‚™์ƒ์„ ๋ฐœ๊ฒฌํ•˜๊ธฐ ์œ„ํ•œ ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋ฐฉ๋ฒ• ์ค‘์—์„œ ์ฐฉ์šฉ์ด ์‰ฝ๊ณ  ์‹ค๋‚ด์™ธ์—์„œ ์ ์šฉ์ด ๊ฐ€๋Šฅํ•œ ์›จ์–ด๋Ÿฌ๋ธ”(Wearable) ์žฅ์น˜์˜ ํ˜•ํƒœ๋ฅผ ๊ณ ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์›จ์–ด๋Ÿฌ๋ธ” ๊ธฐ๋ฐ˜ ๊ฐ€์†๋„ ์„ผ์„œ์™€ ์ž์ด๋กœ ์„ผ์„œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ฐฉ์šฉ์ž์˜ ์›€์ง์ž„์„ ์ธก์ •ํ•˜๊ณ , ๊ฐ€์†๋„ ๋ฐ ๊ฐ์†๋„ ๊ฐ’์„ ๋ถ„์„ํ•˜์—ฌ ๋‚™์ƒ ๋ฐœ์ƒ ์—ฌ๋ถ€๋ฅผ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ฐ์ดํ„ฐ๋ฅผ ํš๋“ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ”ผ์‹คํ—˜์ž์—๊ฒŒ ์›จ์–ด๋Ÿฌ๋ธ” ์žฅ์น˜๋ฅผ ์ฐฉ์šฉํ•œ ์ƒํƒœ๋กœ 12๊ฐ€์ง€ ์ผ์ƒ์ƒํ™œ๋™์ž‘๊ณผ 4๊ฐ€์ง€ ๋‚™์ƒ๋™์ž‘์„ ๋ฐ˜๋ณต์ ์œผ๋กœ ์‹ค์‹œํ•˜๋Š” ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ผ์ƒ์ƒํ™œ๋™์ž‘์€ ์•‰๊ธฐ, ์„œ์žˆ๊ธฐ, ๊ฑท๊ธฐ ๋“ฑ์ด ์žˆ๊ณ , ๋‚™์ƒ๋™์ž‘์€ ์•ž์œผ๋กœ ๋„˜์–ด์ง€๋Š” ๋™์ž‘, ๋’ค๋กœ ๋„˜์–ด์ง€๋Š” ๋™์ž‘ ๋“ฑ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋ฐ์ดํ„ฐ๋ฅผ ํ™•๋ณดํ•˜์˜€๋‹ค. ๋‚™์ƒ๊ณผ ๋น„๋‚™์ƒ ์—ฌ๋ถ€๋ฅผ ๊ฒ€์ถœํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋”ฅ๋Ÿฌ๋‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ชจ๋ธ ์ค‘ ์ˆœํ™˜ ์‹ ๊ฒฝ๋ง(Recurrent Neural Network, RNN) ๊ณ„์—ด์˜ LSTM์„ ํ™œ์šฉํ•œ๋‹ค. ๋‚™์ƒ ์—ฌ๋ถ€๋ฅผ ํŒ๋‹จํ•˜๋Š” LSTM ๋ชจ๋ธ์— ์ ์šฉ๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ์ „์ฒ˜๋ฆฌ ๋ฐ ๋ฏธ์„ธ์กฐ์ •(Fine-Tuning)์„ ํ†ตํ•ด์„œ ๋ชจ๋ธ์„ ๊ณ ๋„ํ™” ํ•˜์˜€๋‹ค. ์‹คํ—˜ ํ™˜๊ฒฝ์—์„œ ๋ชจ๋ธ์˜ ๋‚™์ƒ๊ฐ์ง€ ์ •ํ™•๋„(Accuracy)๋Š” 99.91%๋กœ ์‹ฌ์ธตํ•™์Šต ๊ด€์ ์—์„œ ๋‚™์ƒ ๊ฒ€์ถœ์˜ ํƒ€๋‹น์„ฑ์„ ํŒ๋‹จํ•˜๊ณ ์ž ํ•œ๋‹ค.I. Introduction 1 1.1 Research Background and Objective 1 1.2 Research Scope and Structure of Paper 3 II. Background Knowledge and Related Research 5 2.1 Falls 5 2.2 Fall Detection Techniques 6 2.3 Machine Learning 8 2.4 Recurrent Neural Networks and LSTM 9 III. Methods 13 3.1 Measurement Methods and Devices 13 3.2 Definition of Falls and Daily Living Activities 15 3.3 Development of Fall Detection Model 19 3.4 Performance Evaluation Metrics 21 IV. Results 23 4.1 Data Collection 23 4.2 Data Preprocessing 26 4.3 Model Fine-Tuning 34 4.4 Performance and Results Analysis 36 V. Conclusion 39 5.1 Discussion 39 5.2 Limitations 39 5.3 Future Works 40 Bibliography 43 Abstract in Korean 45์„

    AI and Non AI Assessments for Dementia

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    Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

    Women, Health and Aging: Building a Statewide Movement

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    Provides an overview of current policy and program environments that affect the state's most vulnerable elder population, and considers some effective strategies to address the growing needs of older persons in California

    Models and Analysis of Vocal Emissions for Biomedical Applications

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    The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

    from Issue Investigation to Design Solutions

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2021.8. ์œค๋ช…ํ™˜.๊ฐ€์ „์ œํ’ˆ์„ ํฌํ•จํ•œ ํ˜„๋Œ€ ๊ธฐ์ˆ ์€ ์‚ฌ์šฉ์ž์˜ ์‚ถ์— ํ˜œํƒ์„ ์ œ๊ณตํ•˜์ง€๋งŒ ์ œ์กฐ์—…์ฒด์™€ ์„ค๊ณ„์ž์˜ ์ ‘๊ทผ์„ฑ ์ง€์› ๋ถ€์กฑ์œผ๋กœ ์ธํ•ด ์žฅ์• ์ธ ๋ฐ ๊ณ ๋ น ์‚ฌ์šฉ์ž๋Š” ๊ทธ ํ˜œํƒ์œผ๋กœ๋ถ€ํ„ฐ ์†Œ์™ธ๋˜์—ˆ๋‹ค. ์—ฌ๋Ÿฌ ์‹  ๊ธฐ๋Šฅ์˜ ๊ฐœ๋ฐœ ๋ฐ ๋ฐœ์ „์€ ๋น„์žฅ์• ์ธ ์‚ฌ์šฉ์ž์˜ ์‚ถ์˜ ์งˆ์„ ํ’์š”๋กญ๊ฒŒ ํ•œ ๊ฒƒ๊ณผ๋Š” ๋ฐ˜๋Œ€๋กœ ์ด๋Ÿฌํ•œ ๊ธฐ๋Šฅ๋“ค์€ ๋ณต์žก๋„๊ฐ€ ์ƒํ–ฅ๋˜์–ด ์žฅ์• ์ธ ๋ฐ ๊ณ ๋ น ์‚ฌ์šฉ์ž์˜ ์ ‘๊ทผ์„ฑ๊ณผ ๋…๋ฆฝ์  ์‚ฌ์šฉ์„ ์ €ํ•ดํ•˜๊ณ  ์ด๋‚ด ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์„ ์ €ํ•˜์‹œ์ผฐ์„ ๋ฟ์ด๋‹ค. ์ด์™€ ๊ฐ™์ด ์ ‘๊ทผ์„ฑ ์ง€์›์ด ํ•„์š”ํ•œ ์ƒ์šฉ์ž์˜ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์„ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์€ ์ƒ๊ฐ๋ณด๋‹ค ๋ฒˆ๊ฑฐ๋กœ์šด ์ผ์ด๋‹ค. ๋Œ€์ƒ ์‚ฌ์šฉ์ž๋“ค์€ ๋ฏผ๊ฐํ•œ ๊ฐœ์ธ์ •๋ณด์ƒ์˜ ์ด์œ ๋กœ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜ ์ œ๊ณต์„ ๊บผ๋ฆด ์ˆ˜๋„ ์žˆ๊ณ , ์ธํ„ฐ๋ทฐ๋‚˜ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ์— ์ ํ•ฉํ•œ ์กฐ๊ฑด์ด ์•„๋‹ ์ˆ˜๋„ ์žˆ์œผ๋ฉฐ, ๋” ๋‚˜์•„๊ฐ€ ์†Œํ†ต์— ์–ด๋ ค์›€์ด ์žˆ์„ ์ˆ˜๋„ ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ฌธ์ œ๋Š” ์ œ์กฐ์—…์ฒด๋‚˜ ์„ค๊ณ„์ž์™€ ๊ฐ™์€ ์ดํ•ด๋‹น์‚ฌ์ž์™€ ๋Œ€์ƒ ์‚ฌ์šฉ์ž ๊ฐ„์— ์žฅ๋ฒฝ์„ ๋งŒ๋“ค๊ณ , ์ด๋Ÿฌํ•œ ์žฅ๋ฒฝ์€ ์‚ฌ์šฉ์ž๋“ค์ด ์ผ์ƒ ์ œํ’ˆ์„ ์‚ฌ์šฉํ•˜๋ฉฐ ๊ฒช๊ฒŒ ๋˜๋Š” ๋ฌธ์ œ๋ฅผ ์˜จ์ „ํžˆ ์ดํ•ดํ•˜๊ณ  ์ •์˜ํ•˜๋Š” ๊ฒƒ์„ ์–ด๋ ต๊ฒŒ ๋งŒ๋“ค์–ด ๊ณต๊ฐ์˜ ํ˜•์„ฑ์ด ๋ถˆ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ์ดํ•ด๋‹น์‚ฌ์ž๋“ค์€ ์žฅ์• ๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ, ๊ณ ๋ น์ด ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๊ฒฝํ—˜ํ•ด ๋ณด์ง€ ๋ชป ํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ๋“ค์˜ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์„ ์ž˜๋ชป ํ•ด์„ํ•  ์ˆ˜ ์žˆ๊ณ , ์ด๋Ÿฌํ•œ ๊ณต๊ฐ์˜ ๋ถ€์กฑ์€ ์žฅ์• ์ธ ๋ฐ ๊ณ ๋ น ์‚ฌ์šฉ์ž์— ๋Œ€ํ•œ ํŽธ๊ฒฌ๊ณผ ์˜คํ•ด๋กœ ์ด์–ด์ง„๋‹ค. ๊ฒฐ๊ตญ, ์ ‘๊ทผ ๊ฐ€๋Šฅํ•œ ์ œํ’ˆ ๊ฐœ๋ฐœ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์ œ์กฐ์‚ฌ๋‚˜ ์„ค๊ณ„์ž๊ฐ€ ์ด๋“ค์˜ ๋ถˆํŽธ์‚ฌํ•ญ ๋ฐ ์š”๊ตฌ๋ฅผ ์ธ์ง€ํ•œ๋‹ค ํ•ด๋„ ๋Œ€์ƒ ์‚ฌ์šฉ์ž์˜ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ๋Š” ์–ด๋ ต๊ฑฐ๋‚˜ ์‹ฌ์ง€์–ด ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ๋„ ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋กœ, ๋ณธ ์—ฐ๊ตฌ์˜ 3์žฅ์—์„œ๋Š” ์ธํ„ฐ๋ทฐ์™€ ๊ด€์ฐฐ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐ€์ „์ œํ’ˆ ์‚ฌ์šฉ ๋งฅ๋ฝ์— ๋”ฐ๋ฅธ ๋„ค ๊ฐ€์ง€ ์‚ฌ์šฉ์ž ์œ ํ˜•์— ๋Œ€ํ•œ ์—ฌ๋Ÿ ์ข…๋ฅ˜์˜ ํผ์†Œ๋‚˜๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์‹œ๊ฐ์žฅ์• (์ „๋งน, ์ €์‹œ๋ ฅ), ์ฒญ๊ฐ์žฅ์• (๋†์•„, ์ธ๊ณต ์™€์šฐ), ์ฒ™์ˆ˜์žฅ์• (์ฃผ๋จน ์ฅ” ์†, ํŽด์ง„ ์†), ๊ณ ๋ น์ž(ํ• ๋จธ๋‹ˆ, ํ• ์•„๋ฒ„์ง€) ํผ์†Œ๋‚˜๋Š” ๊ฐ๊ฐ ํผ์†Œ๋‚˜ ์นด๋“œ์˜ ์‹œ๋‚˜๋ฆฌ์˜ค์™€ ๊ฐ™์€ ํ˜•์‹์œผ๋กœ ์ ‘๊ทผ์„ฑ ์ด์Šˆ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์‹ค ์‚ฌ์šฉ์ž์™€ ๋ฉด๋Œ€๋ฉด์œผ๋กœ ๋งŒ๋‚˜๊ธฐ ์–ด๋ ค์šด ์ดํ•ด๋‹น์‚ฌ์ž๋กœ ํ•˜์—ฌ๊ธˆ ๋Œ€์ƒ ์‚ฌ์šฉ์ž์˜ ์ ‘๊ทผ์„ฑ ์ด์Šˆ๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋˜ํ•œ, ์ดํ•ด๋‹น์‚ฌ์ž๋“ค์€ ์‚ฌ์šฉ์ž ์ธํ„ฐ๋ž™์…˜ ๊ด€์ ์—์„œ ์žฅ์• ์ธ ๋ฐ ๊ณ ๋ น ์‚ฌ์šฉ์ž์˜ ๋‹ค๋ฅธ ํ–‰ํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ์ดํ•ดํ•  ๋„๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ 4์žฅ์—์„œ๋Š” ์œ„๊ณ„์  ์ž‘์—…๋ถ„์„(Hierarchical Task Analysis; HTA)์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๊ฐ€์ „์ œํ’ˆ ์‚ฌ์šฉ ์‹œ ์‹œ๊ฐ„ ์ˆœ์„œ์— ๋”ฐ๋ฅธ ์ผ๋ฐ˜์  ์ž‘์—… ๊ตฌ์กฐ๋ฅผ ์ œ์‹œํ•˜์—ฌ ์‚ฌ์šฉ์ž์˜ ์ž‘์—… ํ–‰ํƒœ๋ฅผ ์‹œ๊ฐํ™” ํ•˜์˜€๋‹ค. ์ด ๊ตฌ์กฐ์™€ ํ•จ๊ป˜ ์„œ๋ธ”๋ฆญ(Therblig)์„ ํ†ตํ•ด ์‚ฌ์šฉ์ž์˜ ์ž‘์—…์„ ๋ฏธ์‹œ์ ์œผ๋กœ ํ‘œํ˜„ํ•˜์˜€๋‹ค. ์„œ๋ธ”๋ฆญ์€ ๊ฐ€์ „์ œํ’ˆ ๋งฅ๋ฝ์— ๋งž๋„๋ก ์žฌ์ •์˜ํ•˜๊ณ  ์‚ฌ์šฉ์ž๊ตฐ ๋ณ„๋กœ ๋ฌธ์ œ๊ฐ€ ์žˆ๋Š” ์„œ๋ธ”๋ฆญ์ด ํŒŒ์•…๋œ ๊ฒฝ์šฐ ๋™์ž‘๊ฒฝ์ œ ์›์น™์— ์˜ํ•œ ์„ค๊ณ„ ๊ฐ€์ด๋“œ์— ๋”ฐ๋ผ ๊ฐœ์„ ์•ˆ์„ ์ œ์‹œํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋™์ž‘๊ฒฝ์ œ์›์น™์€ ์‚ฌ์šฉ์ž์˜ ์ž‘์—…์ธก๋ฉด์—์„œ์˜ ๋ฌธ์ œ์ ๊ณผ ์„ค๊ณ„์ธก๋ฉด์—์„œ์˜ ํ•ด๊ฒฐ์•ˆ์„ ์—ฐ๊ด€ ์ง€์–ด ํ•ด์„ํ•˜๋Š” ์ง์„ ๋œ์–ด์ฃผ๋Š” ์—ญํ• ์„ ํ•ด, ์ œ์•ˆํ•˜๋Š” ์ ‘๊ทผ์„ฑ ๋„๊ตฌ๋Š” ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๋„๊ตฌ๋กœ์„œ ํฐ ๊ฐ€์น˜๋ฅผ ๊ฐ€์ง„๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์˜ 5์žฅ์—์„œ๋Š” ๊ธฐ์กด ํ‘œ์ค€๊ณผ ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ˆ˜์ง‘ํ•ด ์„ค๊ณ„ ๊ฐ€์ด๋“œ๋ผ์ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ธฐ์กด ํ‘œ์ค€ ๋ฐ ๊ฐ€์ด๋“œ๋ผ์ธ์€ ์—ฌ๋Ÿฌ ์ˆ˜์น˜๋ฅผ ์ œ๊ณตํ•˜๊ณ ๋Š” ์žˆ์ง€๋งŒ ์žฅ์• ์ธ ๋ฐ ๊ณ ๋ น ์‚ฌ์šฉ์ž์˜ ์‚ฌ์šฉ ๋งฅ๋ฝ์„ ์ถฉ๋ถ„ํžˆ ๋ฐ˜์˜ํ•˜์ง€ ๋ชป ํ•˜๊ณ  ์‚ฌ์šฉ์ž์˜ ์‹ ์ฒด ๋Šฅ๋ ฅ, ํ™˜๊ฒฝ, ์ œํ’ˆ์˜ ํ˜•ํƒœ์— ๋”ฐ๋ผ ์ ์šฉ์ด ์–ด๋ ค์›Œ ์‹ค์ œ์  ํ™œ์šฉ๋„๊ฐ€ ๋‚ฎ์€ ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋˜ํ•œ ์ ‘๊ทผ์„ฑ๊ณผ ์ธ๊ฐ„๊ณตํ•™์  ์ „๋ฌธ์„ฑ์ด ๋ถ€์กฑํ• ์ˆ˜๋ก ์‹ค ์ ์šฉ์ด ์–ด๋ ค์›Œ์ ธ ์ด๋Ÿฌํ•œ ๋ฌธ์„œ์˜ ๊ฐ€์น˜๋Š” ๋”์šฑ ๋‚ฎ์•„์งˆ ์ˆ˜๋ฐ–์— ์—†๋‹ค. ์ด์— ์žฅ์• ์ธ๊ณผ ๊ณ ๋ น์ž์˜ ์‚ฌ์šฉ ๋งฅ๋ฝ์„ ๋ฐ˜์˜ํ•ด ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์žฌ์ •๋ฆฝํ•˜๊ณ  ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด ์ผ๊ณฑ๊ฐ€์ง€์˜ ํ”„๋กœํ† ํƒ€์ž…์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์ด 14๋ช…์˜ ์ฐธ๊ฐ€์ž๊ฐ€ ํ”„๋กœํ† ํƒ€์ž…์„ ํ‰๊ฐ€ํ•˜์—ฌ ๋Œ€์ƒ ๊ฐ€์ „์ œํ’ˆ์˜ ์ ‘๊ทผ์„ฑ ํ–ฅ์ƒ ์—ฌ๋ถ€๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ํ”„๋กœํ† ํƒ€์ž…์€ ์„ฑ๊ณต์ ์œผ๋กœ ์ ‘๊ทผ์„ฑ์— ํ–ฅ์ƒ์„ ๋ณด์—ฌ ์„ค๊ณ„ ๊ฐ€์ด๋“œ๋ผ์ธ์˜ ์œ ํšจ์„ฑ ๋˜ํ•œ ๋ฐ˜์ฆํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์‚ฌ์šฉ๋œ ์ ˆ์ฐจ๋ฅผ ๋”ฐ๋ผ ์ ‘๊ทผ์„ฑ ๋ณด์žฅ ์ œํ’ˆ ์„ค๊ณ„ ์‹œ ๊ฐ ๊ฐ€์ด๋“œ๋ผ์ธ์˜ ์ˆ˜์น˜๋ฅผ ์–ด๋–ค ์‹์œผ๋กœ ์„ค๊ณ„์— ์ ์šฉํ•˜๋Š”์ง€๋ฅผ ์ฐธ๊ณ ํ•  ์ˆ˜๋„ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์˜์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ ์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์‹œ๊ฐ์žฅ์• , ์ฒญ๊ฐ์žฅ์• , ์ฒ™์ˆ˜์žฅ์• ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์‚ฌ์šฉ์ž ์กฐ์‚ฌ๋ฅผ ์ง„ํ–‰ํ•˜๊ณ  ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‚ฌ์šฉ์ž๋“ค์˜ ์ ‘๊ทผ์„ฑ ์ด์Šˆ๋ฅผ ํผ์†Œ๋‚˜ ํ˜•์‹์œผ๋กœ ๊ตฌ์ฒดํ™”ํ•˜์—ฌ ์ดํ•ด๋‹น์‚ฌ์ž๊ฐ€ ๋Œ€์ƒ ์‚ฌ์šฉ์ž์™€ ๋ณด๋‹ค ์‰ฝ๊ฒŒ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๋ณธ ๋…ผ๋ฌธ์€ ์ ‘๊ทผ์„ฑ ์—ฐ๊ตฌ๋ถ„์•ผ์—์„œ ๋ถ€์กฑํ•œ ์ ‘๊ทผ์„ฑ ํ‰๊ฐ€ ๋„๊ตฌ๋ฅผ ์ œ์•ˆํ•˜์—ฌ ์ ‘๊ทผ์„ฑ ์—ฐ๊ตฌ์˜ ์—ฐ๊ตฌ์žฅ๋ฒฝ์„ ๋‚ฎ์ถ”๋Š”๋ฐ ๊ธฐ์—ฌํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹ค์ œ ์ ‘๊ทผ์„ฑ ํ–ฅ์ƒ ์ œํ’ˆ์„ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ๊ฐ€์ด๋“œ๋ผ์ธ๊ณผ ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ œ์ž‘๋œ ํ”„๋กœํ† ํƒ€์ž…์„ ์‹ค์ œ ์‚ฌ์šฉ์ž๋“ค์ด ํ‰๊ฐ€ํ•˜๋„๋ก ํ•ด ๊ฐ€์ด๋“œ๋ผ์ธ์˜ ์‹คํšจ์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ „๋ฐ˜์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ ‘๊ทผ์„ฑ ๋ฌธ์ œ์˜ ์žฅ๋ฒฝ์„ ๋ŒํŒŒํ•˜๊ธฐ ์œ„ํ•ด ์ „๋ฐ˜์ ์ธ ์ œํ’ˆ ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค๋ฅผ ์ ์šฉํ•˜์˜€์œผ๋ฉฐ ์œ ๋‹ˆ๋ฒ„์„ค ๋””์ž์ธ ๊ด€์ ์—์„œ ์ ‘๊ทผ์„ฑ ๋ฌธ์ œ ํ•ด๊ฒฐ์„ ์œ„ํ•œ ์ผ๋ จ์˜ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ์‹์œผ๋กœ ์ œ์•ˆํ•˜์—ฌ ์‚ฌ์šฉ์ž๊ฐ€ ๋ณธ์ธ์˜ ์žฅ์• ๋‚˜ ์—ฐ๋ น๊ณผ ์ƒ๊ด€์—†์ด ์ œํ’ˆ โ€“ ํŠนํžˆ ๊ฐ€์ „์ œํ’ˆ โ€“ ์„ ์ž์œ ๋กญ๊ณ  ์•ˆ์ „ํ•˜๊ฒŒ ์‚ฌ์šฉํ•˜๋„๋ก ํ•˜์˜€๋‹ค.Modern-day technologies - including home appliances - deliver benefits to our lives yet the lack of accessibility supports from the manufacturers and designers have forsaken a considerable number of elderly and disabled people. Unlike how the development and advancement with a variety of new functions and features enriched the quality of life for non-disabled users, it only degraded the user experience for the elderlies and disabled users since such functions and features come along with the increased complexity, which hinders not only the accessible use but also the independent use of a disabled or elderly user. Collecting user experience from the users in need of accessibility support is much more troublesome than one might think. The users may be reluctant to provide their user experience for sensitive privacy reasons, may not be in the appropriate physical conditions for interviews or surveys, or even have communication problems. Such barriers between the stakeholder and the target users do not allow the stakeholders to fully understand and define the problems these users confront every day; simply, impossible to build empathy. The lack of empathy breeds misconceptions on the elderly and disabled users, created by misinterpretation of the usersโ€™ experiences since the stakeholders have never experienced what it is like to be a disabled or elderly user. Even if manufacturers and designers who oversee developing accessible products recognize the needs and frustrations of the disabled population, it is challenging or even inaccessible for them to address these issues of their target customers. In Chapter 3, based on the interview and observation data, this study developed eight personas for four different types of disabled users under the context of home appliance usage: visually impaired (blind and low-vision), hearing impaired (deaf and cochlear implemented), spinal cord injured (opened palm and closed fist), and elderly (grandma and grandpa). Each persona provides their accessibility issues through a persona card and scenario-like explanation. Personas created in this study will help manufacturers and designers empathize with their users although they did not meet the real users face-to-face. Moreover, stakeholders need a tool to investigate how their users in need of accessibility support behave differently from non-disabled users, which provides a deeper understanding of the usersโ€™ perspectives in terms of โ€œinteraction.โ€ In Chapter 4, this study conducted Hierarchical Task Analysis (HTA) and created general task structures of home appliances based on their product compartment and chronological usage phase. This task structure visualizes the user behavior. Combined with the task structure, therbligs expressed the user task on a micro-scale. Therbligs were redefined to fit the home appliance context and, if found problematic, there was the principle of motion economy to provide design guidance to solve the problems of corresponding therbligs. Moreover, the principle of motion economy is valuable because it reduces the burden of a researcher to convert a task-oriented problem found in terms of user behavior into a design-oriented solution. Lastly, in Chapter 5, a design guideline is developed by collecting existing standards and guidelines. Existing standards and documents related to accessibility lack a detailed explanation of real-world application, although the documentations provide various numerical values related to designs. The numbers are not directly implementable since the context-of-use of elderly or disabled users may vary by their capability, environment, and basically by the form factor of the products they use. Lower the expertise in ergonomics and accessibility less valuable the standards and guidelines will be to implement in a product design. With the design guideline developed and ideas collected from an ideation workshop, a total of seven prototypes were built. A total of 14 participants evaluated the prototype whether it enhanced the accessibility of target home appliances or not. As a result, most prototypes successfully improved the accessibility and approved the validity of design guidelines. This procedure as a case study will provide how to implement the principles and dimensional values found in the existing standards and guidelines when developing an accessible product. Overall, this study applied a whole product development cycle to breakthrough the barriers of accessibility problems and proposes it as a set of novel approaches for accessibility issues resolution based on the perspectives of universal design so that a user can freely and safely use their products โ€“ especially home appliances โ€“ regardless of their disability or age.Chapter 1 Introduction 1 1.1 Accessibility Barriers 1 1.1.1 Barriers for Users 1 1.1.2 Barriers for Stakeholders 3 1.2 Research Objectives and Study Outline 12 Chapter 2 Background 15 2.1 Target Users and Products 15 2.1.1 Target Users 15 2.1.2 Target Home Appliances and Compartments 19 2.2 Definition of Accessibility 29 2.3 Design Approach 33 2.3.1 Accessible and Universal Design 33 Chapter 3 Persona to Investigate the Accessibility Issues of Disabled and Elderly Users Under the Context of Home Appliances Usage 35 3.1 Overview 35 3.2 Methods 38 3.2.1 User Data Collection 38 3.2.2 Data Analysis for Personas 42 3.2.3 Persona Creation for Identifying Accessibility Issue 45 3.3 Persona Development 48 3.3.1 User Behaviors and Characteristics 48 3.3.2 Created Personas 53 3.4 Results and Discussion 59 3.4.1 Behaviors and Characteristics of Personas 60 3.4.2 Accessibility Issues from Personas 67 3.5 Probable Applications and Future Studies 77 Chapter 4 TAT: Therbligs as Accessibility Tool 82 4.1 Overview 82 4.1.1 Task Analysis 84 4.1.2 Therbligs and Motion Studies 86 4.1.3 Redefining Therbligs 89 4.1.4 Changes in the Principles of Motion Economy 95 4.2 Methods 102 4.2.1 Therblig-based Task Analysis 103 4.2.2 Task Evaluation 107 4.3 Results 109 4.3.1 General Task Structures 109 4.3.2 Accessibility Evaluation Results 116 4.4 Discussions 122 4.4.1 Problematic Therbligs and Related Principles of Motion Economy for Improvements 125 4.4.2 The Final Set of Therbligs for Accessibility Evaluation 133 4.4.3 New Task Design for Disabled and Elderly Users 139 4.5 Conclusion 142 Chapter 5 Accessible Home Appliance Designs : Prototyping and Design Guidelines 145 5.1 Overview 145 5.2 Ideation for accessible home appliances 148 5.2.1 Ideation Workshop 148 5.2.2 Ideation Result 153 5.3 Development of Design Guidelines and Prototypes 156 5.3.1 Design Guideline Principles 161 5.3.2 Prototyping 173 5.4 Experiment for validation 186 5.4.1 Evaluation Results 188 5.5 Discussion 197 5.6 Conclusion 201 Chapter 6 Conclusion 203 Bibliography 206 ๊ตญ๋ฌธ ์ดˆ๋ก 222 ๊ฐ์‚ฌ์˜ ๊ธ€ 225 Acknowledgment 226 APPENDICES 227๋ฐ•

    Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review

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    In the digital era, human-robot interaction is rapidly expanding, emphasizing the need for social robots to fluently understand and communicate in multiple languages. It is not merely about decoding words but about establishing connections and building trust. However, many current social robots are limited to popular languages, serving in fields like language teaching, healthcare and companionship. This review examines the AI-driven language abilities in social robots, providing a detailed overview of their applications and the challenges faced, from nuanced linguistic understanding to data quality and cultural adaptability. Last, we discuss the future of integrating advanced language models in robots to move beyond basic interactions and towards deeper emotional connections. Through this endeavor, we hope to provide a beacon for researchers, steering them towards a path where linguistic adeptness in robots is seamlessly melded with their capacity for genuine emotional engagement
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