Skip to main content
SpringerLink
Log in

Monitoring system to detect fall/non-fall event utilizing frequency feature from a microwave Doppler sensor: validation of relationship between the number of template datasets and classification performance

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

A fall event is a serious issue for the elderly because it may cause critical aftereffects. To reduce the risk of these aftereffects, early detection of the fall event is essential. However, it is difficult for caregivers to detect fall events early themselves, because they are required to constantly monitor the elderly to confirm their safety. Therefore, an automatic monitoring system which could detect fall events early is helpful in the healthcare field. We have proposed a fall event detection system utilizing a microwave Doppler sensor. The frequency feature is calculated, and compared with known fall or non-fall event data. However, for real-time detection, the number of template datasets must be as low as possible while maintaining high performance of the classification. In this paper, we attempt to identify the relationship between the number of template datasets and the performance of the proposed system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. United Nations, Department of Economic and Social Affairs, Population Division (2015) World population ageing 2015 (ST/ESA/SER.A/390). http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Highlights.pdf. Accessed 16 Nov 2017

  2. Fleming J, Brayne C (2008) Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90. BMJ 337:1279–1282

    Article  Google Scholar 

  3. Doukas CN, Maglogiannis I (2011) Emergency fall incidents detection in assisted living environments utilizing motion, sound, and visual perceptual components. IEEE Trans Inf Technol Biomed 15(2):277–289

    Article  Google Scholar 

  4. Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J (2011) Fall detection with multiple cameras: an occlusion-resistant method based on 3-D silhouette vertical distribution. IEEE Trans Inf Technol Biomed 15(2):290–300

    Article  Google Scholar 

  5. Bianchi F, Redmond SJ, Narayanan MR, Cerutti S, Lovell NH (2010) Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Trans Neural Syst Rehabil Eng 18(6):619–627

    Article  Google Scholar 

  6. Jian H, Chen H (2015) A portable fall detection and alerting system based on k-NN algorithm and remote medicine. China Commun 12(4):23–31

    Article  MathSciNet  Google Scholar 

  7. Cheng J, Chen X, Shen M (2013) A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J Biomed Health Inf 17(1):38–45

    Article  Google Scholar 

  8. Liu J, Lockhart TE (2014) Development and evaluation of a prior-to-impact fall event detection algorithm. IEEE Trans Biomed Eng 61(7):2135–2140

    Article  Google Scholar 

  9. Tong L, Song Q, Ge Y, Liu M (2013) HMM-based human fall detection and prediction method using tri-axial accelerometer. IEEE Sens J 13(5):1849–1856

    Article  Google Scholar 

  10. Aziz O, Robinovitch SN (2011) An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. IEEE Trans Neural Syst Rehabil Eng 19(6):670–676

    Article  Google Scholar 

  11. Shany T, Redmond SJ, Narayanan MR, Lovell NH (2012) Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sens J 12(3):658–670

    Article  Google Scholar 

  12. Cheng WC, Jhan DM (2013) Triaxial accelerometer-based fall detection method using a self-constructing Cascade-AdaBoost-SVM classifier. IEEE J Biomed Health Inf 17(2):411–419

    Article  Google Scholar 

  13. Lee JK, Robinovitch SN, Park EJ (2015) Inertial sensing-based pre-impact detection of falls involving near-fall scenarios. IEEE Trans Neural Syst Rehabil Eng 23(2):258–266

    Article  Google Scholar 

  14. Ozcan K, Mahabalagiri AK, Casares M, Velipasalar S (2013) Automatic fall detection and activity classification by a wearable embedded smart camera. IEEE J Emerg Sel Top Circuits Syst 3(2):125–136

    Article  Google Scholar 

  15. Kau LJ, Chen CS (2015) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Health Inf 19(1):44–56

    Article  Google Scholar 

  16. Cheffena M (2016) Fall detection using smartphone audio features. IEEE J Biomed Health Inf 20(4):1073–1080

    Article  Google Scholar 

  17. Yu M, Rhuma A, Naqvi SM, Wang L, Chambers J (2012) A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans Inf Technol Biomed 16(6):1274–1286

    Article  Google Scholar 

  18. Bai YW, Wu SC, Tsai CL (2012) Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone. IEEE Trans Consum Electron 58(4):1269–1275

    Article  Google Scholar 

  19. Mirmahboub B, Samavi S, Karimi N, Shirani S (2013) Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans Biomed Eng 60(2):427–436

    Article  Google Scholar 

  20. Stone EE, Skubic M (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inf 19(1):290–301

    Article  Google Scholar 

  21. Li Y, Ho KC, Popescu M (2014) Efficient source separation algorithms for acoustic fall detection using a Microsoft Kinect. IEEE Trans Biomed Eng 61(3):745–755

    Article  Google Scholar 

  22. Li Y, Ho KC, Popescu M (2012) A microphone array system for automatic fall detection. IEEE Trans Biomed Eng 59(5):1291–1301

    Article  Google Scholar 

  23. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611–622

    Article  Google Scholar 

  24. Ariani A, Redmond SJ, Chang D, Lovell NH (2012) Simulated unobtrusive falls detection with multiple persons. IEEE Trans Biomed Eng 59(11):3185–3196

    Article  Google Scholar 

  25. Garripoli C, Mercuri M, Karsmakers P, Soh PJ, Crupi G, Vandenbosch GAE, Pace C, Leroux P, Schreurs D (2015) Embedded DSP-based telehealth radar system for remote in-door fall detection. IEEE J Biomed Health Inf 19(1):92–101

    Article  Google Scholar 

  26. Su BY, Ho KC, Rantz MJ, Skubic M (2015) Doppler radar fall activity detection using the wavelet transform. IEEE Trans Biomed Eng 62(3):865–875

    Article  Google Scholar 

  27. Amin MG, Zhang YD, Ahmad F, Ho KCD (2016) Radar signal processing for elderly fall detection: the future for in-home monitoring. IEEE Signal Process Mag 33(2):71–80

    Article  Google Scholar 

  28. Shiba K, Kaburagi T, and Kurihara Y (2017) A novel detection system utilizing frequency distribution collected by microwave Doppler sensors. In: Proceedings of the 22nd international symposium on artificial life and robotics and the 2nd international symposium on bio complexity, Beppu, Oita Japan, Jan 19–21, 2017, vol 5, no 2, pp 16–17

  29. Wang Q (2014) Dynamic time warping (DTW) version 1.4. https://www.mathworks.com/matlabcentral/fileexchange/43156-dynamic-time-warping–dtw-. Accessed 16 Nov 2017

Download references

Author information

Authors and Affiliations

  1. Aoyama Gakuin University, Fuchinobe, Chuo-ku, Sagamihara, Kanagawa, 252-5258, Japan

    K. Shiba, T. Kaburagi & Y. Kurihara

Authors
  1. K. Shiba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Kaburagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Y. Kurihara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Kurihara.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shiba, K., Kaburagi, T. & Kurihara, Y. Monitoring system to detect fall/non-fall event utilizing frequency feature from a microwave Doppler sensor: validation of relationship between the number of template datasets and classification performance. Artif Life Robotics 23, 152–159 (2018). https://doi.org/10.1007/s10015-017-0409-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-017-0409-7

Keywords

Search

Navigation