DOI QR코드

DOI QR Code

스마트폰 가속도 센서를 이용한 강건한 사용자 행위 인지 방법

Robust User Activity Recognition using Smartphone Accelerometer Sensors

  • 투고 : 2013.07.16
  • 심사 : 2013.08.27
  • 발행 : 2013.09.30

초록

최근 몇 년 동안 스마트폰의 등장으로 현대인들의 생활에 많은 변화를 가져왔다. 특히 스마트폰의 센서 정보를 활용하여 사용자의 상황에 맞는 서비스를 제공해주는 응용프로그램들이 많이 등장하고 있다. 스마트폰의 센서 정보는 사용자의 습관이나 행동과 밀접하게 관련되어 있기 때문에 사용자의 상황을 인지하기에 좋은 데이터이다. 현재 모바일 센서 중 GPS 센서는 사용자의 기본적인 행위인지에 많이 활용되고 있다. 하지만 GPS 센서는 사용자의 상황에 따라 수신이 불가능할 수도 있으며 수신된 데이터 역시 부정확할 수 있기 때문에 활용도가 떨어진다. 본 연구에서는 이러한 문제점을 해결하기 위해 모바일 디바이스에 탑재된 가속도 센서 데이터를 중심으로 한 사용자 행위 인지 방법을 제안한다. 가속도 센서는 데이터 수신이 안정적이며, 사용자의 행위에 민감하게 반응하기 때문에 행위인지에 적합하다. 마지막으로 상태 전이도를 활용하여 합리적인 행위변화의 흐름을 적용함으로써 행위인지의 정확도를 높인다.

Recently, with the advent of smart phones, it brought many changes in lives of modern people. Especially, application utilizing the sensor information of smart phone, which provides the service adapted by user situations, has been emerged. Sensor data of smart phone can be used for recognizing the user situation, Because it is closely related to the behavior and habits of the user. currently, GPS sensor one of mobile sensor has been utilized a lot to recognize basic user activity. But, depending on the user situation, activity recognition system cannot receive GPS signal, and also not collect received data. So utilization is reduced. In this paper, for solving this problem, we suggest a method of user activity recognition that focused on the accelerometer sensor data using smart phone. Accelerometer sensor is stable to collect the data and it's sensitive to user behavior. Finally this paper suggests a noble approach to use state transition diagrams which represent the natural flow of user activity changes for enhancing the accuracy of user activity recognition.

키워드

참고문헌

  1. A. M. Khan, Y.-K. Lee, S. Y. Lee, T.-S. Kim, "Human Activity Recognition via An Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis", Future Information Technology (FutureTech), 2010 5th International Conference on, 2010.
  2. Yu-Chieh Yang, Tatsuo Toida, Chin-Ming Hong, "Transportations Prediction Using Build-in Triaxial Accelerometer in Cell Phone", Department of Industrial Education of National Taiwan Normal University, 2010.
  3. Vincenzo Manzoni, Diego Maniloff, Kristian Kloeckl, Carlo Ratti, "Transportation mode identification and real-time co2 emission estimation using smartphones", Massachusetts Institute of Technology (MIT), 2011.
  4. Leon Stenneth, Ouri Wolfson, Philip S. Yu, Bo Xu, "Transportation Mode Detection using Mobile Phones and GIS Information", GIS '11 Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp.54-63, 2011.
  5. Pekka Siirtola, Juha Roning, "Recognizing Human ActivitIes User-indenpendently on Smartphones Based on Accelerometer Data", International Journal of Interactive Multimedia and Artificial Intelligence, pp.38-45, 2012.
  6. Arvind Thiagarajan, James Biagioni, Tomas Gerlich, Jakob Eriksson, "Cooperative Transit Tracking using Smart-phones", SenSys '10 Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp.85-98, 2010.
  7. Ben Nham, Kanya Siangliulue, Serena Yeung, "Predicting Mode of Transport from iPhone Accelerometer Data", Stanford University CS229 Machine Learning, 2008.
  8. Jennifer R. Kwapisz, Gary M. Weiss, Samuel A. Moore, "Activity recognition using cell phone accelerometers", ACM SIGKDD Explorations Newslette Vol.12, pp.74-82, 2010.
  9. Fehmi Ben Abdesslem, Andrew Philips, Tristan Henderson, "Less is More: Energy-Efficient Mobile Sensing with SenseLess", MobiHeld '09 Proceedings of the 1st ACM workshop on Networking, systems, and applications for mobile handhelds, pp.61-62, 2009.
  10. Adam J. Aviv, Benjamin Sapp, Matt Blaze, Jonathan M. Smith, "Practicality of Accelerometer Side Channels on Smartphones", ACSAC '12 Proceedings of the 28th Annual Computer Security Applications Conference, pp.41-50, 2012.
  11. Yang, Jun, "Toward physical activity diary: motion recognition using simple acceleration features with mobile phones", Proceeding of the 1st international workshop on Interactive multimedia for consumer electronics, pp.1-10, 2009.
  12. David Mizell, "Using Gravity to Estimate Accelerometer Orientation", Proceedings of the Seventh IEEE International Symposium on Wearable Computers(ISWC'03), 2005.
  13. Xi Long, Student Member, IEEE, Bin Yin, Ronald M Aarts, Fellow, IEEE, "Single-Accelerometer-Based Daily Physical Activity Classification", 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.
  14. Scuola Superiore Sant'Anna, Piazza Martiri della Liberta, "Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers", Computational Intelligence and Neuroscience, Vol.10, 2010.
  15. Mehmet Sonercan, Sinan Dincer, "User State Tracking using Smartphones", the degree of Bachelor of Science in Bogazici University Computer Networks Research Laboratory, 2011.
  16. Sian Lun Lau, Klaus DAVID, "Movement Recognition using the Accelerometer in Smartphones", Future Network & MobileSummit 2010 Conference Proceedings Paul Cunningham and Miriam Cunningham(Eds) IIMC International Information Management Corporation, 2010.