||A Study on Activity and Mood Recognition in Healthcare Settings Using Smartphones
||Institute of Computer Science and Information Engineering
實驗評估主要使用10次交叉驗證(10-fold cross-validation) 之準確性來選擇最佳的分類方法並且進行實際個案施測。本文所提之方法架構與實現系統，在糖尿病個案施測部分，實驗組具顯著性的活動量提升。本研究之日常生活記錄/日常行為操作相關資料庫、實驗結果及研究探討，可提供行動健康照護電腦資訊處理學者具系統化的設計與發展該架構於相關研究並整合，更可將所研發之成果提供行動健康照護之相關議題應用，另一方面則將能廣泛運用於各種領域之應用中，創造研究創新之價值。
In the recent years, with the continuous development of wireless communication and mobile devices is becoming increasingly popular. Wireless health is very important and to assist development in multidisciplinary research. Mobile healthcare technology will be the key to change user's behavior and increase for self-management of wellness via mobile devices. The platform provides convenient tools to record subjects’ physical activity, mental health status, and deliver customized recommendation. Clinicians can observe information on how many physical activities and mental health status to make better recommendations for them.
The purpose of this study is to develop an activity pattern recognition system to assist clinicians via machine learning technology to automatically identify and record physical activity and emotional status for assisting improvement subjects' healthy. Theories in feature extraction, feature selection, machine learning technology, selection classifier and Cognitive Behavioral Therapy combination to provide physical activity services of the physical activity recognition system. Specifically, the study was aimed to: 1) develop activity and mood recognition system. Using machine learning technology to extract the features and developing the recognize model for physical activity recognition and mood recognition, 2) develop a communication platform for patients and clinicians, 3) apply the physical activity recognition system and mood recognition to type 2 diabetes patients and patients with mild depression, and 4) after the system intervention, we focus on data analysis and discussion.
For the assessment of the method, we used the precision rate of the 10-fold cross-validation to select classification and recruit subjects. Experimental results show that the proposed approaches give an encouraging improvement in its tasks. Case study also shows that the literacy aptitude test and the performance of reading comprehension were significantly improved. The outcomes are expected to provide useful information for wireless health researchers and computer scientists to develop the related assistive technology, and also contribute to useful application in the future. The outcomes are expected to provide useful information for wireless health researchers and computer scientists to develop the related assistive technology, and also contribute to useful application in the future.
摘 要 i
LIST OF TABLES ix
LIST OF FIGURES x
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Purpose and Specific Aims 3
1.3 Organization of Dissertation 3
Chapter 2. Literature Review 5
2.1 Physical Activity Measurement Methods 5
2.2 Wireless Health 6
2.3 Physical Activity Recognition 7
2.4 Emotion Recognition 8
2.5 Mobile Health Applications for Health Monitoring with Cloud Support 11
Chapter 3. Activity Behavior Database Development 13
3.1 Development of Activity Behavior Database 13
3.2 Physical Activity Data Collection 14
3.2.1 Design Considerations 15
3.2.2 Activity Definition 15
3.2.3 Activity Data Collection and Exploring 16
3.3 Smartphone Usage Patterns Collection and Emotion Tagging 18
3.3.1 Design Considerations 18
3.3.2 Emotion Definition 18
3.3.3 Emotional Data Collection and Exploring 20
Chapter 4. Activity Recognition Using Accelerometer-enabled Smartphone 21
4.1 Retrieval Daily Physical Activity 22
4.1.1 Feature Extraction 24
4.1.2 Feature Selection 25
4.2 Comparison of Classifier Methods 27
4.2.1 Data Description 28
4.2.2 The Classifiers 29
4.2.3 Comparison of classifiers 29
4.2.4 Results of activity pattern recognition 30
4.3 Summary 33
Chapter 5. Daily Emotional Recognition Using Smartphone Usage Patterns 34
5.1 Development of Retrieval Daily Emotion 34
5.2 Methods for Classifier Selection 35
5.2.1 Data Description 35
5.2.2 Classification 36
5.2.3 Comparison of classifiers 38
5.3 Summary 41
Chapter 6. Experimental Results and Discussion 42
6.1 Experiments on the Physical Activity Pattern Recognition System 42
6.1.1 Design of Experiments for the Physical Activity Recognition System 42
6.1.2 Participants 44
6.1.3 Principal Findings 45
6.1.4 Limitations 50
6.2 Experiments on the Negative Emotions Detection System 50
6.2.1 Design of Experiments for the Negative Emotions Detection System 50
6.2.2 Participants 51
6.2.3 Principal Findings 52
6.2.4 Limitations 54
Chapter 7. Conclusions and Future Study 58
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