Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Video-based fall detection algorithm combining simple threshold method and Hidden Markov Model

단순 임계치와 은닉마르코프 모델을 혼합한 영상 기반 낙상 알고리즘

  • Park, Culho (Department of Electrical, Electronic and Control Engineering and IITC, Hankyong National University) ;
  • Yu, Yun Seop (Department of Electrical, Electronic and Control Engineering and IITC, Hankyong National University)
  • Received : 2014.06.03
  • Accepted : 2014.07.16
  • Published : 2014.09.30
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Abstract

Automatic fall-detection algorithms using video-data are proposed. Six types of fall-feature parameters are defined applying the optical flows extracted from differential images to principal component analysis(PCA). One fall-detection algorithm is the simple threshold method that a fall is detected when a fall-feature parameter is over a threshold, another is to use the HMM, and the other is to combine the simple threshold and HMM. Comparing the performances of three types of fall-detection algorithm, the algorithm combining the simple threshold and HMM requires less computational resources than HMM and exhibits a higher accuracy than the simple threshold method.

영상 정보를 이용한 자동 낙상 감지 알고리즘을 제안한다. 자동으로 낙상을 감지하기 위한 낙상 특징 파라미터를 추출하기 위해서 영상정보를 광류 방식에 적용하여 움직임 값들을 추출하고 이 움직임 값들에 대한 전체적인 변화의 정도와 기울기, 중심점을 주성분 분석 방법으로 계산한다. 계산된 고유값과 고유 벡터를 사용하여 6가지 낙상 특징 파라미터를 정의한다. 이 낙상특징파라미터가 미리 정해둔 임계값을 초과하는 경우를 낙상으로 판단하는 단순 임계치 방법과 낙상특징파라미터를 은닉 마르코프 모델(Hidden Markov Model; HMM)에 적용시켜 낙상을 판단하는 방법과 단순임계치와 은닉 마르코프 모델을 결합한 낙상 감지 방법을 제안하고 그 결과를 비교 및 분석한다. 단순 임계치와 은닉 마르코프 모델을 결합한 방법은 단순임계치 방법으로 낙상 가능한 행동들을 결정하고 이 결정된 낙상 행동들만을 은닉 마르코프 모델을 적용하여 낙상을 감지한다. 이 방법은 계산량을 줄이면서 감지 정확도를 유지하는 결과를 보인다.

Keywords

References

  1. C. A. Werner, "The Older Population: 2010", 2010 Census Briefs U.S. Bureau of the Census. Available: http://www.census.gov/prod/cen2010/briefs/c2010br-09.pdf
  2. R. Igual, C. Medrano, and I. Plaza, "Challenges, issues and trends in fall detection systems," Biomed. Eng. Online, vol. 12, pp. 66, 2013. https://doi.org/10.1186/1475-925X-12-66
  3. M. Kangas, I. Vikman, J. Wiklander, P. Lindgren, L. Nyberg, T. Jamsa, "Sensitivity and specificity of fall detection in people aged 40 years and over," Gait & Posture, vol. 29, pp. 571-574, 2009. https://doi.org/10.1016/j.gaitpost.2008.12.008
  4. Y. J. Yi and Y. S. Yu, "Emergency-monitoring system based on newly-developed fall detection algorithm," J. Inf. Commun. Converg. Eng., vol. 11, no. 3, pp. 147-154, 2013.
  5. L. Tong, Q. Song, Y. Ge, and M. Liu, "HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer," IEEE Sensors J., vol. 13, no. 5, pp.1849-1856, 2013. https://doi.org/10.1109/JSEN.2013.2245231
  6. S.-R. Ke, H. L. U. Thuc, Y.-J. Lee, J.-N. Hwang, J.-H. Yoo, K.-H.Choi, "A Review on Video-Based Human Activity Recognition," Computers, vol. 2, pp. 88-131, 2013. https://doi.org/10.3390/computers2020088
  7. B. U. Toreyin, Y. Dedeo lu and A. E. Cetin, "HMM Based Falling Person Detection Using Both Audio and Video," Lecture Notes in Computer Science, Vol. 3766, pp. 211-220, 2005. https://doi.org/10.1007/11573425_21
  8. Z. Fu, T. Delbruck, P. Lichtsteiner, E. Culurciello, "An address-event fall detector for assisted living applications," IEEE Trans. Biomedical Circuits and Systems, Vol. 2, No. 2, pp. 88-96, 2008. https://doi.org/10.1109/TBCAS.2008.924448
  9. S.-G. Miaou, P.-H. Sung, and C.-Y. Huang, "A customized human fall detection system using omni-camera images and personal information," in Proc. 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference, April 2006, pp. 39-42.
  10. N. H. Kim and Y. S. Yu, "Video based fall detection algorithm using Hidden Markov Model", Journal of The Institute of Electronics Engineers of Korea, vol 50, no. 8, pp. 2160-2165, 2013. https://doi.org/10.5573/ieek.2013.50.8.232
  11. J. Jackson, A user's guide to principal components, New York: John Wiley & Sons, 1991.
  12. J. Shi and C. Tomasi, "Good Features To Track", In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVRP94), pp.593-600, June 1994.
  13. G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, Beijing: O'REILLY, 2009.
  14. L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of IEEE, vol. 77, no. 2, pp. 257-286, 1989. https://doi.org/10.1109/5.18626
  15. M. Goonen, "Receiver operating characteristic (ROC) curves," in Proc. SUGI 31, March 2006, pp.210-231. Available:http://www2.sas.com/proceedings/sugi31/210-31.pdf.