스마트미디어저널 (Smart Media Journal)

  • 제7권4호
  • /
  • Pages.79-89
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  • 2018
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  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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A Fall Detection Technique using Features from Multiple Sliding Windows

  • Pant, Sudarshan (Dept. of Multimedia Engineering, Graduate School, Mokpo National University) ;
  • Kim, Jinsoo (Graduate School of Computer Information Security, Chonbuk National University) ;
  • Lee, Sangdon (Dept. of Multimedia Engineering, College of Engineering, Mokpo National University)
  • 투고 : 2018.08.30
  • 심사 : 2018.11.17
  • 발행 : 2018.12.31
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초록

In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a more accurate fall detection system and an effective feature extraction technique are required to prevent and reduce the risk of such incidents. In this paper, we proposed an efficient feature extraction technique based on multiple sliding windows and validated it through a series of experiments using supervised learning algorithms. The experiments were conducted using the public datasets obtained from tri-axial accelerometers. The results depicted that extraction of the feature from adjacent sliding windows led to high accuracy in supervised machine learning-based fall detection. Also, the experiments conducted in this study suggested that the best accuracy can be achieved by keeping the window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors, the experiments achieved accuracy rates of 94%.

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참고문헌

  1. WHO. Falls (2018). http://www.who.int/en/news-room/fact-sheets/detail/falls (accessed Aug., 27, 2018).
  2. Mao A.; Ma X.; He Y.; Luo J.; " Highly portable, Sensor-Based System for Human Fall Monitoring, Sensors," Sensors, 2017.
  3. He, J.; Bai, S.; Wang, X.; "An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier," Sensors, 2017.
  4. Putra, P.; Brusey, J.; Gaura, E.; Vesilo, R.; "An Event Triggered Machine Learning Approach for Accelerometer-Based Fall Detection," Sensors, 2018.
  5. Diep, N.; Pham, C.; Phuong, T.; "A classifier based approach to real-time fall detection using low-cost wearable sensors.," Proc. Of International Conference on Soft Computing and Pattern Recognition(SoCPaR), pp.105-110, 2013.
  6. Dinh, C.; Struck, M.; "A New Real-time Fall Detection Approach Using Fuzzy and neural network," Proc. Of International Conference on Wearable Micro and Nano Technologies for Personalized Health, pp.57-60, 2009.
  7. Putra, P.; Vesilo, R.; "Window-size impact on detection rate of wearable-sensor based fall detection using supervised machine learning.," Proc. Of IEEE Life Sciences Conference, pp.21-26, 2017.
  8. Li, Q.; Stankovic, J.; Hanson, M.; Barth, A.; Lach, J.; Zhou G.; "Accurate, Fast Fall Detection using Gyroscopes and Accelerometer Derived Posture Information," Body Sensor Network, International Workshop on Wearable and Implantable Body Sensor Networks, pp.138-143, 2009.
  9. Lindemann, U.; Hock, A.; Stuber, M.; Keck, W.; Becker, C.; "Evaluation of a Fall Detector Based on Accelerometers: A Pilot Study," Med. Biol. Eng. Coput., vol.43, pp.548-551, 2005. https://doi.org/10.1007/BF02351026
  10. Bagala, F.; Becker, C.; Cappello, A.; Chiari, L.; Aminian, K.; Hausdorff, J.; Zijlstra, W.; Klenk, J.; "Evaluation of Accelerometer-based Fall Detection Algorithms on Real-world Falls.," PLoS ONE, 2012.
  11. Ojetola, O.; Gaura, E.; Brusey, J.; "Fall Detection with Wearable Sensors-SAFE (SmArt Fall dEtection)," Proc. Of International Conference on Intelligent Environments(IE), pp.318-321, 2011.
  12. Tong L.; Song, Q.; Ge, Y.; Liu M.; "HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer.," IEEE Sens. J., vol.13, pp.1249-1256 vol.1, 2013.
  13. Habib, M.A.; Mohktar, M.S.; Kamaruzzaman, S.B.; Lim, K.S.; Pin, T.M.; Ibrahim, F.; "Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues," Sensors, vol.14, pp.7181-7208, 2014. https://doi.org/10.3390/s140407181
  14. Tae Woong Kim, "Group Behavior Pattern and Activity Analysis System Using Big Data Based Acceleration Signals," Smart Media Journal, vol.6, no.3, pp.83-88, 2017.
  15. Younghun Lee, Yongil Kim, "Design of Building Biomertic Big Data System using the Mi Band and MongoDB," Smart Media Journal, vol.5, no.4, pp.124-130, 2016.
  16. Kangas, M.; Vikman, I.; Wiklander, J.; Lindgren, P.; Nyberg, L.; Jamsa, T.; Sensitivity and Specificity of Fall Detection in People Aged 40 Years and Over.," Gait Posture, vol.29, no.4, pp. 571-574, 2009. https://doi.org/10.1016/j.gaitpost.2008.12.008
  17. Ojetola, O.; Gaura, E.; Brusey, J.; "Data Set for Fall Events and Daily Activities from Inertial Sensors.," Proc. Of ACM Multimedia Systems Conference, pp.243-248, 2015.
  18. Pedregosa, F.; Varaquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Douborg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E.; "Scikit-learn: Machine Learning in Python.," Journal of Machine Learning Research, vol.12, pp. 2825-2830, 2011.
  19. Gjoreski, M.; Lustrek, M.; Gams, M.; "Accelerometer Placement for Posture Recognition and Fall Detection.," Proc. Of International Conference on Intelligent Environment(IE), pp.47-54, 2011.