Fall detection system using Kinect’s infrared sensor


This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    BBC News-Microsoft Kinect ‘fastest-selling device on record’. Last accessed 17 May 2011

  2. 2.

    Openkinect. Last accessed 17 May 2011

  3. 3.

    OpenNI. Last accessed 17 May 2011

  4. 4.

    Primesense. Last accessed 17 May 2011

  5. 5.

    Alwan, M., Rajendran, P., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: IEEE Proceedings of International Conference on Information and Communication Technologies: From Theory to Applications, pp. 1003–1007 (2006)

  6. 6.

    Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., Meunier, J.: 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 (2011)

  7. 7.

    Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)

  8. 8.

    Cucchiara, R., Prati, A., Vezzani, R.: An intelligent surveillance system for dangerous situation detection in home environments. Intell. Artif. 1(1), 11–15 (2004)

  9. 9.

    Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Exp. Syst. J. 24, 334–345 (2007)

  10. 10.

    Deandrea, S., Lucenteforte, E., Bravi, F., Foschi, R., La Vecchia, C., Negri, E.: Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology 21(5), 658–668 (2010)

  11. 11.

    Diraco, G., Leone, A., Siciliano, P.: An active vision system for fall detection and posture recognition in elderly healthcare. In: Proceedings of the Conference on Design, Automation and Test in Europe, pp. 1536–1541 (2010)

  12. 12.

    Donald, I., Bulpitt, C.: The prognosis of falls in elderly people living at home. Age Ageing 28(2), 121–5 (1999)

  13. 13.

    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

  14. 14.

    Foroughi, H., Aski, B., Pourreza, H (2008) Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: International Conference on Computer and Information Technology, pp. 219–224

  15. 15.

    Hori, T., Nishida, Y., Aizawa, H.: Sensor network for supporting elderly care home. In: IEEE Proceedings of Sensors 2, 575–578 (2004)

  16. 16.

    Jansen, B., Deklerck, R.: Context aware inactivity recognition for visual fall detection. In: Proceedings of International Conference on Pervasive Computing Technologies for Healthcare, pp. 1–4 (2006)

  17. 17.

    Jansen B., Temmermans F., Deklerck R.: 3D human pose recognition for home monitoring of elderly. In: IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 4049–4051 (2007)

  18. 18.

    Kim, K.J., Ashton-Miller, J.A.: Biomechanics of fall arrest using the upper extremity: age differences. Clin. Biomech. 18(4), 311–318 (2003)

  19. 19.

    Leclercq, S.: In-company same- and low-level falls: from an understanding of such accidents to their prevention. Int. J. Ind. Ergonomics 25(1), 59–67 (2000)

  20. 20.

    Li, Q., Stankovic, J., Hanson, M., Barth, A., Lach, J., Zhou, G.: Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks, pp. 138–143 (2009)

  21. 21.

    Londei, S.T., Rousseau, J., Ducharme, F., St-Arnaud, A., Meunier, J., Saint-Arnaud, J., Giroux, F.: An intelligent videomonitoring system for fall detection at home: perceptions of elderly people. J. Telemed. Telecare 15, 383–390 (2009)

  22. 22.

    Popescu, M., Li, Y., Skubic, M., Rantz, M.: An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In: IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 4628–4631 (2008)

  23. 23.

    Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) Toward Useful Services for Elderly and People with Disabilities. Lecture Notes in Computer Science, vol. 6719, pp. 121–128. Springer, Berlin (2011)

  24. 24.

    Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Monocular 3D head tracking to detect falls of elderly people. In: IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 6384–6387 (2006)

  25. 25.

    Schumer, M.A., Steiglitz, K.: Adaptive step size random search. IEEE Trans. Autom. Control 13(3), 270–276 (1968)

  26. 26.

    Sixsmith, A., Johnson, N.: A smart sensor to detect the falls of the elderly. IEEE Pervasive Comput 3(2), 42–47 (2004)

  27. 27.

    Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: HMM based falling person detection using both audio and video. In: Sebe, N., Lew, M.S., Huang, T.S. (eds.) IEEE International Workshop on Human–Computer Interaction. Lecture Notes in Computer Science, vol. 3766, pp. 211–220. Springer, Berlin (2005)

  28. 28.

    Vishwakarma, V., Mandal, C., Sural, S.: Automatic detection of human fall in video. In: Proceedings of International Conference on Pattern Recognition and Machine Intelligence, PReMI’07, pp. 616–623. Springer, Berlin (2007)

  29. 29.

    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill (1995)

  30. 30.

    Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. J. Biomech. 33(11), 1497–1500 (2000)

  31. 31.

    Xiao, Y., Siebert, P., Werghi, N.: A discrete reeb graph approach for the segmentation of human body scans. In: IEEE Proceedings of International Conference on 3-D Digital Imaging and Modeling (2003)

  32. 32.

    Zhao, J., Katupitiya, J., Ward, J.: Global correlation based ground plane estimation using v-disparity image. In: IEEE Intl. Conf. on Rob. and Aut., pp. 529–534 (2007)

  33. 33.

    Zouba, N., Boulay, B., Bremond, F., Thonnat, M.: Cognitive vision. In: Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures, pp. 37–50. Springer, Berlin (2008)

Download references


We thank Dr. Jesus Martinez del Rincon and Andy Strong for their invaluable assistance in setting up the experimental environment for the capturing sessions.

Author information

Correspondence to Georgios Mastorakis.

Electronic supplementary material

Below is the link to the electronic supplementary material.

AVI (465 KB)

AVI (465 KB)

AVI (995 KB)

AVI (995 KB)

AVI (671 KB)

AVI (671 KB)

AVI (1591 KB)

AVI (1591 KB)

AVI (411 KB)

AVI (411 KB)

AVI (559 KB)

AVI (559 KB)

AVI (420 KB)

AVI (420 KB)

AVI (1941 KB)

AVI (1941 KB)

AVI (773 KB)

AVI (773 KB)

AVI (786 KB)

AVI (786 KB)

AVI (491 KB)

AVI (491 KB)

AVI (563 KB)

AVI (563 KB)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Mastorakis, G., Makris, D. Fall detection system using Kinect’s infrared sensor. J Real-Time Image Proc 9, 635–646 (2014).

Download citation


  • Kinect
  • Fall detection
  • Home assistance
  • Real-time processing
  • 3D bounding box analysis