earticle

영어

Purpose: In this study, we investigated state-of-the-art lifelog analysis methods and describe a modeling process for analysis of acceleration data. These data can be easily obtained from smart devices and is useful for patients with visual display terminal (VDT) syndrome, who show specific lifestyle patterns. Methods and Results: We reviewed 23 recent lifelog articles focused on preprocessing, feature extraction, and classification to design a model for analysis of lifestyle patterns associated with VDT syndrome. Based on our review of articles, we recommend using relatively simple statistical indices, including min, max, median variance, and standard deviation, or frequency indices, such as power spectrum analysis for feature extraction. Based on favorable results with large datasets reported by several previous studies, we suggest using a genetic algorithm (GA) for classification. Notably, establishment of an organized human resource system for systematic data collection and management can improve data quality and also learning efficiency. Conclusion: We recommend the use of simple statistical indices and a GA for feature extraction and classification, respectively, to design a model for analysis of lifestyle patterns in patients with VDT syndrome. We also emphasize the importance of establishing an organized human resource system for systematic data collection and management to improve data quality and learning efficiency.