Browse > Article
http://dx.doi.org/10.5573/ieek.2013.50.6.254

Fall Recognition Algorithm Using Gravity-Weighted 3-Axis Accelerometer Data  

Kim, Nam Ho (Department of Software Engineering, DongyangMirae University)
Yu, Yun Seop (Department of Electrical, Electronic and Control Engineering, Hankyong National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.6, 2013 , pp. 254-259 More about this Journal
Abstract
A newly developed fall recognition algorithm using gravity weighted 3-axis accelerometer data as the input of HMM (Hidden Markov Model) is introduced. Five types of fall feature parameters including the sum vector magnitude(SVM) and a newly-defined gravity-weighted sum vector magnitude(GSVM) are applied to a HMM to evaluate the accuracy of fall recognition. A GSVM parameter shows the best accuracy of falls which is 100% of sensitivity and 97.96% of specificity, and comparing with SVM, the results archive more improved recognition rate, 5.2% of sensitivity and 4.5% of specificity. GSVM shows higher recognition rate than SVM due to expressing falls characteristics well, whereas SVM expresses the only momentum.
Keywords
fall recognition; 3-axis accelerometer; Hidden Markov Model; fall feature parameters;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 B. Kaluza, M. Luštrek, "Fall detection and activity recognition methods for the confidence project: a survey," the 12th International Multiconference Information Society 2008, vol. A, pp. 22-25, 2008.
2 G. Yinyu and N.-H. Kim, "A Study on Wavelet-based Image Denoising Using a Modified Adaptive Thresholding Method ," J. lnf. Commun. Converg. Eng. vol. 10, no.1, pp.45-52, 2012.
3 M. Popescu, Y. Li, M. Skubic, M. Rantz, "An Acoustic Fall Detector System that Uses Sound Height Information to Reduce the False Alarm Rate," 30th Int. IEEE EMBS Conf., pp. 4628-4631, Vancouver, BC, Aug. 20-24, 2008.
4 A. K. Bourke, C. N. Scanaill, K. M. Culhane, J. V. O'Brien, and G. M. Lyons. "An optimum accelerometer configuration and simple algorithm for accurately detecting falls." in Pro. of the 24th IASTED international Conference on Biomedical Engineering, pp. 156-160, Innsbruck, Austria, Feb. 15-17, 2006.
5 M. Kangas, A. Konttila, P. Lindgren, I. Winblad, T. Jamsa. "Comparison of low-complexity fall detection algorithms for body attached accelerometers." Gait & Posture, Vol. 28, issue 2, pp. 285-291, 2008.   DOI   ScienceOn
6 D. J. Willis. Ambulation Monitoring and Fall Detection System using Dynamic Belief Networks. PhD Thesis. School of Computer Science and Software Engineering, Monash University, 2000.
7 T. Zhang, J. Wang, P. Liu and J. Hou, "Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm," International Journal of Computer Science and Network Security, Vol. 6, no. 10, 2006.
8 이영설 ,손동운, 조성배, "계층적 은닉 마르코프 모델을 이용한 이동 센서 기반 행동 인식," 정보과학회논문지: 컴퓨팅의 실제 및 레터 제17권 제4호, pp.279-283, 2011.   과학기술학회마을
9 김상기, 박건혁, 전석희, 임성훈, 한갑종, 최승문, 최승진, "3차원 가속도 데이터를 이용한 HMM 기반의 동작인식," 정보과학회논문지, 제15권, 제3호, 216-220쪽, 2009년 3월   과학기술학회마을
10 J. Yang, "Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phone," the 1st International Workshop on Interactive Multimedia for Consumer Electronics 2009, pp. 1-10, Beijing, China, 2009.
11 BMA150 Triaxial acceleration sensor Data sheet. Bosch Sensortec.
12 M. Tapia, S. Intille, W. Haskell, K. Larson, J. Wright, A. King, and R. Friedman, "Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor", the 11th IEEE International Symposium on Wearable Computers, pp. 37-40, 2007.
13 A. K. Bourke, G. M. Lyons, "A threshold-based fall detection algorithm using a bi-axial gyroscope sensor", Medical Engineering & Physics, vol. 30, issue 1, pp.84-90, 2006.