Alzheimer’s disease detection using skeleton data recorded with Kinect camera

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease that leads to defects in cognitive and functional abilities of elderly people. In this paper, a novel methodology is presented to detect Alzheimer’s disease using recorded skeleton data with a KinectV.2 camera from the subject’s gait. After clinical assessment, the single-task walking test done by subjects was recorded with the kinectV.2 camera. Then, some descriptive statistical analyses were performed on the extracted features of recorded gait to compare them between people with Alzheimer’s disease and people without any cognitive impairment as the healthy control (HC) group. Then, a support vector machine classifier with different kernels was designed to classify subjects to AD and HC groups. The results show that the proposed method has acceptable results in comparison to previous studies to detect AD. The proposed method in this article has the accuracy, sensitivity, precision, and specificity of 92.31%, 96.33%, 88.62%, and 90.81% respectively to classify subjects to AD and HC groups.

This is a preview of subscription content, access via your institution.

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

Notes

  1. 1.

    Mann–Whitney U.

References

  1. 1.

    Aramendi, A., Aztiria, A., Basarab, A.: On the early diagnosis of Alzheimer’s disease from multimodal signals: a survey. Artif. Intell. Med. 71, 1–29 (2016)

    Article  Google Scholar 

  2. 2.

    Alzheimer’s Association: 2017Alzheimer’s disease facts and figures. Alzheimer’s & Dement. 13(4), 325–373 (2017)

    Article  Google Scholar 

  3. 3.

    Lee, L, Grimson, W.E.: Gait analysis for recognition and classification. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC pp. 1–8 (2002)

  4. 4.

    Valkanova, V., Ebmeier, K.P.: What can gait tell us about dementia? Review of epidemiological and neuropsychological evidence. Gait Posture 53, 215–223 (2017)

    Article  Google Scholar 

  5. 5.

    Pourghayoomi, E., Negahdar, F., Shahidi, G., Mehraban, A.H., Ebrahimi, I., Taghizadeh, G., et al.: Correlation between functional balance and mobility tests and postural sway measures in dual task paradigm in Parkinson’s disease (a pilot study). J. Basic Clin. Pathophysiol. 2(2), 1–12 (2014)

    Google Scholar 

  6. 6.

    Herran, A.M., Garcia-Zapirain, B., Mendez-Zorrilla, A.: Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14(2), 3362–3395 (2014)

    Article  Google Scholar 

  7. 7.

    Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12(2), 2255–2283 (2012)

    Article  Google Scholar 

  8. 8.

    Middleton, L., Buss, A.A, Bazin, A., Nixon, M.S.: A floor sensor system for gait recognition. In: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies. Buffalo, pp. 171–176 (2005)

  9. 9.

    Chen, S.W., Lin, S.H., Liao, L.D., Lai, H.Y., Pei, Y.C., Kuo, T.S., et al.: Quantification and recognition of parkinsonian gait from monocular video imaging using kernel based principal component analysis. Biomed. Eng. Online 10(99), 1–21 (2011)

    Google Scholar 

  10. 10.

    Schneider, B., Banerjee, T.: Activity recognition using imagery for smart home monitoring. In: Advances in Soft Computing and Machine Learning in Image Processing, Studies in Computational Intelligence, Springer. pp. 355–371 (2017)

  11. 11.

    Imani, Z., Soltanizadeh, H.: Person reidentification using local pattern descriptors and anthropometric measures from videos of Kinect sensor. IEEE Sens. J. 16(16), 6227–6238 (2016)

    Article  Google Scholar 

  12. 12.

    Akl, A., Taati, B., Mihailidis, A.: Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE Trans. Biomed. Eng. 62(5), 1383–1394 (2015)

    Article  Google Scholar 

  13. 13.

    Wang, W.H., Wu, H.L., Chung, P.C., Pai, M.: An HMM-based gait comparison: using Alzheimer’s disease patients as examples. In: Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). Killarney, pp. 1–6 (2015)

  14. 14.

    Wang, W.H., Hsu, Y.L., Pai, M.C., Wang, C.H., Wang, C.Y., Lin, C.W., et al.: Alzheimer’s disease classification based on gait information. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN). Beijing, pp. 3251–3257 (2014)

  15. 15.

    Varatharajan, R., Manogaran, G., Priyan, M.K.: Wearable sensor devices for early detection of Alzheimer’s disease using dynamic time warping algorithm. Clust. Comput. 21(1), 681–690 (2017)

    Article  Google Scholar 

  16. 16.

    Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing, 2nd edn. California Technical Publishing, San Diego (1999)

    Google Scholar 

  17. 17.

    Chambers, H.G., Sutherland, D.H.: A practical guide to gait analysis. J. Am. Acad. Orthop. Surg. 10(3), 222–231 (2002)

    Article  Google Scholar 

  18. 18.

    Nguyen, T.N., Huynh, H.H., Meunier, J.: Skeleton-based abnormal gait detection. Sensors 16(11), 1792–1804 (2016)

    Article  Google Scholar 

  19. 19.

    Salleh, N.M., Rahman, Z.A., Mohd Rani, M.D.: Basic Statistics for Medical and Health Sciences, 1st edn. USIM, Kuala Lumpur (2013)

    Google Scholar 

  20. 20.

    Xie, J., Wang, C., Zhang, Y., Jiang, S.: Clustering support vector machines for unlabeled data classification. In: 2009 International Conference on Test and Measurement. Hong Kong, pp. 34–38 (2009)

  21. 21.

    Liu, S., Jiang, N.: SVM parameters optimization algorithm and its application. In: 2008 IEEE International Conference on Mechatronics and Automation. Takamatsu, pp. 509–513 (2008)

  22. 22.

    Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process. 5(2), 1–11 (2015)

    Article  Google Scholar 

  23. 23.

    Ries, J.D., Echternach, J.L., Nof, L., Blodgett, M.G.: Test-retest reliability and minimal detectable change scores for the timed “Up & Go” test, the six-minute walk test, and gait speed in people with Alzheimer disease. Phys. Ther. 89(6), 569–579 (2009)

    Article  Google Scholar 

  24. 24.

    Hsu, Y.L., Chung, P.C., Wang, W.H., Pai, M.C., Wang, C.Y., Lin, C.W., et al.: Gait and balance analysis for patients with Alzheimer’s disease using an inertial-sensor-based wearable instrument. IEEE J. Biomed. Health Inform. 18(6), 1822–1830 (2014)

    Article  Google Scholar 

  25. 25.

    Borges, S.D., Radanovic, M., Forlenza, O.V.: Correlation between functional mobility and cognitive performance in older adults with cognitive impairment. Aging Neuropsychol. Cognit. 25(1), 23–32 (2016)

    Article  Google Scholar 

  26. 26.

    Taylor, M.E., Lasschuit, D.A., Lord, S.R., Delbaere, K., Kurrle, S.E., Mikolaizak, A.S., et al.: Slow gait speed is associated with executive function decline in older people with mild to moderate dementia: a one year longitudinal study. Arch. Gerontol. Geriatr. 73, 148–153 (2017)

    Article  Google Scholar 

  27. 27.

    Webster, K.E., Merory, J.R., Wittwer, J.E.: Gait variability in community dwelling adults with Alzheimer’s disease. Alzheimer Dis. Assoc. Disord. 20(1), 37–40 (2006)

    Article  Google Scholar 

  28. 28.

    Maquet, D., Lekeu, F., Warzee, E., Gillain, S., Wojtasik, V., Salmon, E., et al.: Gait analysis in elderly adult patients with mild cognitive impairment and patients with mild Alzheimer’s disease: simple versus dual task: a preliminary report. Clin. Physiol. Funct. Imaging 30(1), 51–56 (2010)

    Article  Google Scholar 

  29. 29.

    Gillain, S., Drame, M., Lekeu, F., Wojtasik, V., Ricour, C., Criosier, J.L.: Gait speed or gait variability, which one to use as a marker of risk to develop Alzheimer disease? A pilot study. Aging Clin. Exp. Res. 28(2), 249–255 (2016)

    Article  Google Scholar 

  30. 30.

    Gras, L.Z., Kanaan, S.F., McDowd, J.M., Colgrove, Y.M., Burns, J., Pohl, P.S.: Balance and gait of adults with very mild Alzheimer’s disease. J. Geriatr. Phys. Ther. 38(1), 1–7 (2015)

    Article  Google Scholar 

  31. 31.

    Cedervall, Y., Halvorsen, K., Aberg, A.C.: A longitudinal study of gait function and characteristics of gait disturbance in individuals with Alzheimer’s disease. Gait Posture 39(4), 1022–1027 (2014)

    Article  Google Scholar 

  32. 32.

    Ardlea, R.M., Morris, R., Hickey, A., Dina, S.D., Koychev, I., Gunnc, R.N.: Gait in mild Alzheimer’s disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study. J. Alzheimer’s Dis. 63(4), 331–341 (2018)

    Article  Google Scholar 

  33. 33.

    Boripuntakul, S., Lord, S.R., Brodie, M.A., Smith, S.T., Methapatara, P., Wongpakaran, N., et al.: Spatial variability during gait initiation while dual tasking is increased in individuals with mild cognitive impairment. J. Nutr. Health Aging 18(3), 307–312 (2014)

    Article  Google Scholar 

  34. 34.

    König, A., Klaming, L., Pijl, M., Demeurraux, A., David, R., Robert, P.: Objective measurement of gait parameters in healthy and cognitively impaired elderly using the dual-task paradigm. Aging Clin. Exp. Res. 29(6), 1181–1189 (2017)

    Article  Google Scholar 

  35. 35.

    Merory, J.R., Wittwer, J.E., Rowe, C.C., Webster, K.E.: Quantitative gait analysis in patients with dementia with Lewy bodies and Alzheimer’s disease. Gait Posture 26(3), 414–419 (2007)

    Article  Google Scholar 

  36. 36.

    Gillain, S., Warzee, E., Lekeu, F., Wojtasik, V., Maquet, D., Croisier, J.L., et al.: The value of instrumental gait analysis in elderly healthy, MCI or Alzheimer’s disease subjects and a comparison with other clinical tests used in single and dual-task conditions. Ann. Phys. Rehabil. Med. 52(6), 453–474 (2009)

    Article  Google Scholar 

  37. 37.

    Rucco, R., Agosti, V., Jacini, F., Sorrentino, P., Varriale, P., Stefano, M.D.: Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer’s disease through 3D motion capture. Gait Posture 52, 312–317 (2016)

    Article  Google Scholar 

  38. 38.

    Muir, S.W., Speechley, M., Wells, J., Borrie, M., Gopaul, K., Montero-Odasso, M.: Gait assessment in mild cognitive impairment and Alzheimer’s disease: the effect of dual-task challenges across the cognitive spectrum. Gait Posture 35(1), 96–100 (2012)

    Article  Google Scholar 

  39. 39.

    Choi, J.S., Oh, H.S., Kang, D.W., Mun, K.R., Choi, M.H., Lee, S.J., et al.: Comparison of gait and cognitive function among the elderly with Alzheimer’s disease, mild cognitive impairment and healthy. Int. J. Precis. Eng. Manuf. 12, 169–173 (2011)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to thank the Iran Alzheimer’s Association (IAA) for its contribution to this study. In particular, we would like to thank all the subjects in this research and their families who agree to participate in this study.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hadi Soltanizadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Seifallahi, M., Soltanizadeh, H., Hassani Mehraban, A. et al. Alzheimer’s disease detection using skeleton data recorded with Kinect camera. Cluster Comput 23, 1469–1481 (2020). https://doi.org/10.1007/s10586-019-03014-z

Download citation

Keywords

  • Alzheimer detection
  • Gait analysis
  • Kinect camera
  • Classification
  • Support vector machine