Earticle

초록

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.

목차

Abstract
Ⅰ. Introduction
Ⅱ. Related Previous Study
Ⅲ. Analyze and classification model of lifelog for preventing VDT syndromes
Ⅳ. Conclusion
Acknowledgment
참고문헌

키워드

저자

• Jaehyuk Lee [ 이재혁 | Hanshin University ]
• Jongmin Lim [ 임종민 | Hanshin University ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  정형스포츠물리치료학회지 [Archives of Orthopedic and Sports Physical Therapy]

• 간기

  반년간

• pISSN

  1738-5946

• 수록기간

  2005~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 517 DDC 613