access icon openaccess Method to classify elderly subjects as fallers and non-fallers based on gait energy image

Falls are one of the leading causes of injuries among the elderly. Therefore, distinguishing fallers and performing preventive actions is vitally important. A new variation of the gait energy image (GEI) called coloured gait energy image (CGEI) is proposed for classifying subjects as fallers and non-fallers and for visualising their gait patterns. Eight elderly fallers, eight elderly non-fallers and eight young subjects performed timed up and go (TUG) test, which is one of the well-known clinical tools for fall risk assessment and contains two gait sequences. Subjects were also asked to perform two other variations of the TUG test, namely TUG with manual load and TUG with cognitive load. Gait sequences were extracted from the TUG test based on the opinion of three human observers. Then the gait cycles were automatically extracted from the walking sequence and divided into three phases, corresponding to double support and first and second half of single support. Next, the GEI of each phase was generated and formed one of the colour components of CGEI. Histogram-based features obtained from CGEI were then used to classify the video collected from walking sequences of elderly fallers and non-fallers. Correct classification rate was improved by approximately 27% compared with the standard TUG test.

Inspec keywords: image sequences; gait analysis; data visualisation; image classification; image colour analysis; geriatrics; medical image processing

Other keywords: gait pattern visualization; fall risk assessment; correct classification rate; colour components; walking sequence; TUG test; CGEI; clinical tools; gait cycles; gait energy image; cognitive load; elderly subject classification; histogram-based features; gait sequences; nonfallers; timed up and go test; coloured gait energy image; GEI

Subjects: Graphics techniques; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Image recognition; Biology and medical computing; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Biomedical measurement and imaging

References

    1. 1)
      • 4. Stone, E., Skubic, M.: ‘Fall detection in homes of older adults using the Microsoft Kinect’, IEEE J. Biomed. Health Inf., 2014, (99), doi: 10.1109/JBHI.2014.2312180.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 7. Shumway-Cook, A., Brauer, S., Woollacott, M.: ‘Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test’, Phys. Ther., 2000, 80, (9), pp. 896903.
    7. 7)
    8. 8)
    9. 9)
      • 1. Lord, S., Sherringtone, C., Menz, H.B.: ‘Falls in older people: risk factor and strategies for prevention’ (Cambridge University Press, Cambridge, UK, 2001).
    10. 10)
    11. 11)
      • 6. Millor, N., Lecumberri, P., Gomez, M., Martinez-Ramirez, A., Izquierdo, M.: ‘Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review’, IEEE Trans. Neural Syst. Rehabil. Eng., 2014, (22), doi: 10.1109/TNSRE.2014.2331895.
    12. 12)
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2014.0065
Loading

Related content

content/journals/10.1049/htl.2014.0065
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading