access icon free Detecting heel strikes for gait analysis through acceleration flow

In some forms of gait analysis, it is important to be able to capture when the heel strikes occur. In addition, in terms of video analysis of gait, it is important to be able to localise the heel where it strikes on the floor. In this study, a new motion descriptor, acceleration flow, is introduced for detecting heel strikes. The key frame of heel strike can be determined by the quantity of acceleration flow within the region of interest, and positions of the strike can be found from the centre of rotation caused by radial acceleration. Our approach has been tested on a number of databases which were recorded indoors and outdoors with multiple views and walking directions for evaluating the detection rate under various environments. Experiments show the ability of our approach for both temporal detection and spatial positioning. The immunity of this new approach to three anticipated types of noises in real CCTV footage is also evaluated in our experiments. The authors acceleration flow detector is shown to be less sensitive to Gaussian white noise, whilst being effective with images of low-resolution and with incomplete body position information when compared with other techniques.

Inspec keywords: object detection; image motion analysis; Gaussian noise; video signal processing; gait analysis; closed circuit television; white noise

Other keywords: motion descriptor; key frame; temporal detection; walking directions; multiple views; acceleration flow quantity; spatial positioning; region-of-interest; real CCTV footage; Gaussian white noise; heel strikes detection; acceleration flow detector; gait analysis

Subjects: Computer vision and image processing techniques; Closed circuit television; Other topics in statistics; Optical, image and video signal processing; Video signal processing; Other topics in statistics

References

    1. 1)
      • 34. Fortun, D., Bouthemy, P., Kervrann, C.: ‘Optical flow modeling and computation: a survey’, Comput. Vis. Image Underst., 2015, 134, pp. 121.
    2. 2)
      • 16. Komura, T., Nagano, A., Leung, H., et al: ‘Simulating pathological gait using the enhanced linear inverted pendulum model’, IEEE Trans. Biomed. Eng., 2005, 52, (9), pp. 15021513.
    3. 3)
      • 12. Yam, C., Nixon, M., Carter, J.: ‘Gait recognition by walking and running: a model-based approach’. Asian Conf. on Computer Vision, Melbourne, Australia, January 2002, pp. 16.
    4. 4)
      • 23. Sun, Y., Hare, J.S., Nixon, M.S.: ‘Detecting acceleration for gait and crime scene analysis’. Int. Conf. on Imaging for Crime Detection and Prevention, Madrid, Spain, 2016.
    5. 5)
      • 30. Shutler, J.D., Grant, M.G., Nixon, M.S., et al: ‘On a large sequence-based human gait database’, in Lotfi, A., Garibaldi, J. M. (Eds.): ‘Applications and Science in Soft Computing’ (Springer, Berlin, 2004), pp. 339346.
    6. 6)
      • 10. Lu, J., Wang, G., Moulin, P.: ‘Human identity and gender recognition from gait sequences with arbitrary walking directions’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (1), pp. 5161.
    7. 7)
      • 18. Hreljac, A., Marshall, R.N.: ‘Algorithms to determine event timing during normal walking using kinematic data’, J. Biomech., 2000, 33, (6), pp. 783786.
    8. 8)
      • 24. Baker, S., Scharstein, D., Lewis, J.P., et al: ‘A database and evaluation methodology for optical flow’, Int. J. Comput. Vis., 2011, 92, (1), pp. 131.
    9. 9)
      • 31. Iwama, H., Okumura, M., Makihara, Y., et al: ‘The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (5), pp. 15111521.
    10. 10)
      • 21. Bouchrika, I., Nixon, M.S.: ‘Model-based feature extraction for gait analysis and recognition’, in Gagalowicz, A., Philips, W. (Eds.): ‘‘Computer Vision/Computer Graphics Collaboration Techniques’ (Springer, Berlin, 2007), pp. 150160.
    11. 11)
      • 27. Cunado, D., Nixon, M.S., Carter, J.N.: ‘Automatic extraction and description of human gait models for recognition purposes’, Comput. Vis. Image Underst., 2003, 90, (1), pp. 141.
    12. 12)
      • 3. Skelly, M.M., Chizeck, H.J.: ‘Real-time gait event detection for paraplegic FES walking’, IEEE Trans. Neural Syst. Rehabil. Eng., 2001, 9, (1), pp. 5968.
    13. 13)
      • 2. Whittle, M.W.: ‘Normal ranges for gait parameters’, in Whittle, M. W (Ed): ‘Gait analysis: an introduction’ (Elsevier, London, 2007, 4th edn.).
    14. 14)
      • 28. Yu, S., Tan, T., Huang, K., et al: ‘A study on gait-based gender classification’, IEEE Trans. Image Process., 2009, 18, (8), pp. 19051910.
    15. 15)
      • 1. Nixon, M.S., Tan, T., Chellappa, R.: ‘Human identification based on gait’ (Springer, 2005).
    16. 16)
      • 22. Jung, S.U., Nixon, M.S.: ‘Heel strike detection based on human walking movement for surveillance analysis’, Pattern Recognit. Lett., 2013, 34, (8), pp. 895902.
    17. 17)
      • 5. Shull, P.B., Jirattigalachote, W., Hunt, M.A., et al: ‘Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention’, Gait Posture, 2014, 40, (1), pp. 1119.
    18. 18)
      • 4. Chau, T., Rizvi, S.: ‘Automatic stride interval extraction from long, highly variable and noisy gait timing signals’, Hum. Mov. Sci., 2002, 21, (4), pp. 495514.
    19. 19)
      • 9. Auvinet, E., Multon, F., Aubin, C.E., et al: ‘Detection of gait cycles in treadmill walking using a kinect’, Gait Posture, 2015, 41, (2), pp. 722725.
    20. 20)
      • 15. Han, J., Bhanu, B.: ‘Individual recognition using gait energy image’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (2), pp. 316322.
    21. 21)
      • 26. Kajita, S., Matsumoto, O., Saigo, M.: ‘Real-time 3D walking pattern generation for a biped robot with telescopic legs’. Proc. 2001 IEEE Int. Conf. on Robotics and Automation (ICRA), Seoul, South Korea, 2001, vol. 3, pp. 22992306.
    22. 22)
      • 8. O'Connor, C.M., Thorpe, S.K., O'Malley, M.J., et al: ‘Automatic detection of gait events using kinematic data’, Gait Posture, 2007, 25, (3), pp. 469474.
    23. 23)
      • 20. Bamberg, S., Benbasat, A.Y., Scarborough, D.M., et al: ‘Gait analysis using a shoe-integrated wireless sensor system’, IEEE Trans. Inf. Technol. Biomed., 2008, 12, (4), pp. 413423.
    24. 24)
      • 25. Weinzaepfel, P., Revaud, J., Harchaoui, Z., et al: ‘DeepFlow: large displacement optical flow with deep matching’. Proc. IEEE Int. Conf. on Computer Vision, Sydney, Australia, 2013, pp. 13851392.
    25. 25)
      • 29. Wang, L., Ning, H., Tan, T., et al: ‘Fusion of static and dynamic body biometrics for gait recognition’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (2), pp. 149158.
    26. 26)
      • 7. Rueterbories, J., Spaich, E.G., Larsen, B., et al: ‘Methods for gait event detection and analysis in ambulatory systems’, Med. Eng. Phys., 2010, 32, (6), pp. 545552.
    27. 27)
      • 19. Pappas, I.P.I., Popovic, M.R., Keller, T., et al: ‘A reliable gait phase detection system’, IEEE Trans. Neural Syst. Rehabil. Eng., 2001, 9, (2), pp. 113125.
    28. 28)
      • 14. Bobick, A.F., Davis, J.W.: ‘The recognition of human movement using temporal templates’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (3), pp. 257267.
    29. 29)
      • 11. Wang, J., She, M., Nahavandi, S., et al: ‘A review of vision-based gait recognition methods for human identification’. Int. Conf. on Digital Image Computing: Techniques and Applications, Sydney, Australia, 2010, pp. 320327.
    30. 30)
      • 32. Bouchrika, I., Nixon, M.S.: ‘Gait-based pedestrian detection for automated surveillance’. Proc. Int. Conf. on Computer Vision Systems, Bielefeld, Germany, 2007.
    31. 31)
      • 33. Sun, D., Roth, S., Black, M.J.: ‘A quantitative analysis of current practices in optical flow estimation and the principles behind them’, Int. J. Comput. Vis., 2014, 106, (2), pp. 115137.
    32. 32)
      • 13. Wang, L., Tan, T., Hu, W., et al: ‘Automatic gait recognition based on statistical shape analysis’, IEEE Trans. Image Process., 2003, 12, (9), pp. 11201131.
    33. 33)
      • 17. Zeni, J.A., Richards, J.G., Higginson, J.S.: ‘Two simple methods for determining gait events during treadmill and overground walking using kinematic data’, Gait Posture, 2008, 27, (4), pp. 710714.
    34. 34)
      • 6. Djurić-Jovičić, M.D., Jovičić, N.S., Popović, D.B.: ‘Kinematics of gait: new method for angle estimation based on accelerometers’, Sensors, 2011, 11, (11), pp. 1057110585.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0429
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0429
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading