Inhalt

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   Acknowledgements
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   Introduction
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  Research Plan for Automatic Journaling of Daily Activities
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   Motivation
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   Goals and Research Questions
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   Definition of Daily Activities
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   Definition of Daily Activities for this Project
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   Applications for the Planned System
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   Summary
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  State-of-the-Art Activity Recognition
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  Technical Background
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   Computing Power and Storage
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   Small Sensors and Devices Available
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  Related Work
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   Life-Logging
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   Quantified Self Community
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   Localization, Location, and Transportation
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   Physical Activity Tracking from Accelerometer Data
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   Medical Use of Diary Systems
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  Research Systems
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   Memex Vision by Vannevar Bush
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   MyLifeBits by Microsoft
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   SenseCam by Microsoft
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  Commercial Systems
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   Classic Diary and Digital Counterparts
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   Fitbit Ultra
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   Facebook (Timeline)
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   Summary
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  Mobile Platforms and Technology Overview
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   Requirement Analysis
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  Platform Selection
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   Introduction
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   Apple – iOS
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   Google – Android
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   RIM – BlackBerry OS
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   HP / Palm – webOS
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   Microsoft – Windows Phone 7
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   Other Mobile Platforms
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   Conclusion
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  Hardware Requirements and Selection of a Suitable Smartphone
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   Overview
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   Requirements
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   Selection
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   Model Specifications and Sensors
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  Considerations for Software / User Interfaces
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   User Interface
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   Implementation
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  Thoughts on System Limitations & Resource Efficiency
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   Accuracy of Phone Sensors
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   Limitations of the Phone
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   Resource Efficiency
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   Extending the System
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   Smart Environments
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   Computer / TV Usage
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   Summary
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  Development of a System to Collect Daily Activity Data
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   User Study Motivation and Goals
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   Upcoming Challenges
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   Experiment Design
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   Preliminary Questionnaire
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   Preliminary Questionnaire Results
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   Conclusive Experiment Design
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  Software Implementation
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   Experiment Software Operating Mode
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   User Interface
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   Background Data Logging
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   Summary
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   Experimental Procedure
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   Current Activity
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   Well-Being
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   General Experiment Questionnaire
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  Experiment Conditions and Specifications
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   Experiment Questionnaires Results
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   Experiment Questionnaires Conclusion
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   First Experiences and Lessons Learned
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  Analysis and Evaluation of the Collected Daily Activity Data
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   Packing Raw Sensor Data and Computing Features
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   Data Overview and Structuring Daily Activity Data
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  Visualization and Evaluation of Daily Activity Data
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   Data Visualization of One Full Day
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   In-Depth Evaluation of Location Data
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   Evaluation of Other Feature Data
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   Activity Category Combinations and Transitions
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   Conclusion
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  Classification of Daily Activities Using Sensor Data
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   Classification Methods for Multi-Label Problems
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   Evaluation Metrics for Multi-Label Classification
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   Classification Results
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   Refining the Classification Approach
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   Discussion and Conclusion
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  Development of the AMARAS System
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   Re-defined Concepts and Goals
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   Application Implementation Details
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   Application Overview
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   Background Service
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   User Interface
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   Feature Extraction from Raw Sensor Data
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   On-Device Classification of Daily Activities
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   Technical Details
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   Application Summary
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  Evaluation of the AMARAS Application
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   Research Questions for the Study
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   Study Conditions
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   Notes and Problems During the Study
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   Questionnaire Results of the Final User Study
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   Classification Results Compared to the Last Study
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   In-Study Performance Analysis
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   Possible Enhancements
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   Summary, Discussion, and Conclusion
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  Summary, Conclusion and Outlook
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   Summary
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   Conclusion
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   Outlook
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   First Study
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   Second Study