KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Smartphone-User Interactive based Self Developing Place-Time-Activity Coupled Prediction Method for Daily Routine Planning System

일상생활 계획을 위한 스마트폰-사용자 상호작용 기반 지속 발전 가능한 사용자 맞춤 위치-시간-행동 추론 방법

  • 이범진 (서울대학교 컴퓨터공학부) ;
  • 김지섭 (서울대학교 컴퓨터공학부) ;
  • 류제환 (서울대학교 뇌과학협동과정) ;
  • 허민오 (서울대학교 컴퓨터공학부) ;
  • 김주석 (삼성전자 빅데이터센터) ;
  • 장병탁 (서울대학교 컴퓨터공학부)
  • Received : 2014.09.15
  • Accepted : 2014.11.19
  • Published : 2015.02.15
⟨ Previous Next ⟩

Abstract

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

과거 어플리케이션 다양성만 지향하던 사용자의 수요가 최근 스마트폰의 고도화된 센서와 기계학습이 결합된 지능형 어플리케이션으로의 선호로 전향되고 있다. 이러한 경향을 반영하여 본 논문에서는 스마트폰에 축적된 사용자의 라이프로깅 데이터에서 의미있는 정보를 추출하고, 추출한 정보를 통해 사용자의 인지적 행동을 대신 가능한 인지 에이전트(Cognitive Agent)개념의 스마트폰-사용자 상호작용 사용자 맞춤 위치-시간-행동 추론 기법을 제안한다. 제안 방법은 사용자의 라이프로깅데이터를 DPGMM (Dirichlet Process Gaussian Mixture Model) 클러스터링 기법으로 사용자 주요 관심지역 POI(Point of Interest)를 자동으로 추출하고, 평생학습이 가능한 강화학습의 한 종류인 POMDP(Partially Observable Markov Decision Process)를 사용하여 사용자의 위치-시간-행동을 추론 한다. 제안 방법으로 구현한 사용자 맞춤 일과 계획 시스템의 시간별 사용자 일과 추론 결과는 70%이상의 성능을 보였으며, 하루 일과 계획 지능형 서비스의 새로운 방향을 제시하고 있다.

Keywords

Acknowledgement

Supported by : 미래창조과학부

References

  1. G. Hache, E. D. Lemaire, N. Baddour, "Mobility Change-of-state Detection using a Smartphonebased Approach," Proc. of the IEEE International Workshop on Medical Measurements and Applications, pp. 43-46, 2010.
  2. H. Verkasalo, "Analysis of Smartphone User Behavior," Proc. of Mobile Business and 9th Global Mobility Roundtable, pp. 258-263, 2010.
  3. A. Papliatseyeu, M. Oscar, "Mobile Habits: Inferring and Predicting User Activities with a Locationaware Smartphone," 3rd Symposium of Ubiquitous Computing and Ambient Intelligence, Vol. 51, pp. 343-352, 2009.
  4. B. Longstaff, S. Reddy, D. Estrin, "Improving Activity Classification for Health Applications on Mobile Devices using Active and Semi-supervised Learning," Proc. of Pervasive Computing Technologies for Healthcare, pp. 1-7, 2010.
  5. G. M. Weiss, J. Lockhart, "The Impact of Personalization on Smartphone based Activity Recognition," AAAI Workshop on Activity Context Representation: Techniques and Languages, 2012.
  6. Y. Lee and S.-B. Cho, "Activity Recognition using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer," Hybrid Artificial Intelligent Systems, pp. 460-467, Springer Berlin Heidelberg, 2011.
  7. S.-I. Yang, J.-H. Hong, S.-B. Cho, "Activity Recognition based on Multi-modal Sensors using Dynamic Bayesian Networks," Journal of KIISE: Computing Practices and Letters, Vol. 15, No. 1, pp. 72-76, 2009. (in Korean)
  8. S. Kaghyan, H. Sarukhanyan, "Activity Recognition using K-nearest Neighbor Algorithm on Smartphone with Tri-axial Accelerometer," Journal of IJIMA : ITHEA International Scientific Society, Vol. 1, pp. 146-156, 2012.
  9. B. T. Zhang, "Communication as Moving Target Tracking," Alignment in Communication: Towards a New Theory of Communication, pp. 133-148, John Benjamins Publishing, 2013.
  10. B. T. Zhang, "Information-Theoretic Objective Functions for Lifelong Learning," AAAI Spring Symposium on Lifelong Machine Learning, pp. 62-69, 2013.