139
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system

&
Pages 62-72 | Received 25 Apr 2021, Accepted 08 Sep 2022, Published online: 20 Sep 2022

References

  • Bandini, A., Orlandi, S., Escalante, H. J., Giovannelli, F., Cincotta, M., Reyes-Garcia, C. A., Vanni, P., Zaccara, G., & Manfredi, C. (2017). Analysis of facial expressions in Parkinson’s disease through video-based automatic methods. Journal of Neuroscience Methods, 281, 7–20. http://dx.doi.org/10.1016/j.jneumeth.2017.02.006
  • Benmalek, E., Elmhamdi, J., & Jilbab, A. (2018). Multiclass classification of Parkinson’s disease using cepstral analysis. International Journal of Speech Technology, 21(1), 39–49. https://doi.org/10.1007/s10772-017-9485-2
  • Buongiorno, D., Bortone, I., Cascarano, G. D., Trotta, G. F., Brunetti, A., & Bevilacqua, V. (2019). A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s disease. BMC Medical Informatics and Decision Making, 19(Suppl 9), 243. https://doi.org/10.1186/s12911-019-0987-5
  • Cancela, J., Fico, G., & Waldmeyer, M. T. A. (2015). Using the ieanalytic hrarchy process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson’s disease. BMC Medical Informatics and Decision Making, 153(Suppl 3), S7. https://doi.org/10.1186/1472-6947-15-S3-S7
  • Debû, B., Godeiro, C. D. O., Lino, J. C., & Moro, E. (2018). Managing gait, balance, and posture in Parkinson’s disease. Current Neurology and Neuroscience Reports, 18(23), 1–12. https://doi.org/10.1007/s11910-018-0828-4
  • Diallo, O., Rodrigues, J. J. P. C., Sene, M., & Niu, J. (2014). Real-time query processing optimization for cloud-based wireless body area networks. Information Sciences, 284, 84–94. http://dx.doi.org/10.1016/j.ins.2014.03.081
  • Fisher, J. M., Hammerla, N. Y., Ploetz, T., Andras, P., Rochester, L., & Walker, R. W. (2016). Unsupervised home monitoring of Parkinson’s disease motor symptoms using body-worn accelerometers. Parkinsonism and Related Disorders, 33, 44–50. http://dx.doi.org/10.1016/j.parkreldis.2016.09.009
  • Gómeza, P., Mekyska, J., Gómez, A., Palacios, D., Rodellar, V., & Álvarez, A. (2019). Characterization of Parkinson’s disease dysarthria in terms of speecharticulation kinematics. Biomedical Signal Processing and Control, 52, 312–320. https://doi.org/10.1016/j.bspc.2019.04.029
  • Hellqvist, C., & Berterö, C. (2015). Support supplied by Parkinson’s disease specialist nurses to Parkinson’s disease patients and their spouses. Applied Nursing Research, 28(2), 86–91. http://dx.doi.org/10.1016/j.apnr.2014.12.008
  • Hussain, A., Wenbi, R., Silva, A. L. D., Nadher, M., & Mudhish, M. (2015). Health and emergency-care platform for the elderly and disabled people in the smart city. The Journal of Systems and Software, 110, 253–263. http://dx.doi.org/10.1016/j.jss.2015.08.041
  • Kamran, I., Naz, S., Razzak, I., & Imran, M. (2021). Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Future Generation Computer Systems, 117, 234–244. https://doi.org/10.1016/j.future.2020.11.020
  • Karan, B., Sahu, S. S., Orozco-Arroyave, J. R., & Mahto, K. (2020). Hilbert spectrum analysis for automatic detection and evaluation ofParkinson’s speech. Biomedical Signal Processing and Control, 61, 102050. https://doi.org/10.1016/j.bspc.2020.102050
  • Khlebtovsky, A., Djaldetti, R., Rodity, Y., Keret, O., Tsvetov, G., Slutzcki-Shraga, I., & Benninger, F. (2017). Progression of postural changes in Parkinson’s disease: Quantitative assessment. Journal of Neurology, 264(4), 675–683. https://doi.org/10.1007/s00415-017-8402-6
  • Kim, Y., & Lee, S. (2014). Energy-efficient wireless hospital sensor networking for remote patient monitoring. Information Sciences, 282, 332–349. http://dx.doi.org/10.1016/j.ins.2014.05.056
  • Kruger, R., Klucken, J., Weiss, D., Tonges, L., Kolber, P., Unterecker, S., Lorrain, M., Baas, H., Muller, T., & Rieder, P. (2017). Classification of advanced stages of Parkinson’s disease: Translation into stratified treatments. Journal of Neural Transmission, 124(8), 1015–1027. https://dx.doi.org/10.1007/2Fs00702-017-1707-x
  • Li, Q., Wang, Y., Sharf, A., Cao, Y., Tu, C., Chen, B., & Yu, S. (2018). Classification of gait anomalies from kinect. The Visual Computer, 34(2), 229–241. https://doi.org/10.1007/s00371-016-1330-0
  • Machado, I. P., Gomes, A. L., Gamboa, H., Paixão, V., & Costa, R. M. (2015). Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization. Information Processing and Management, 51(2), 204–214. http://dx.doi.org/10.1016/j.ipm.2014.07.008
  • Mamun, K. A. A., Alhussein, M., Sailunaz, K., & Islam, M. S. (2017). Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Future Generation Computer Systems, 66, 36–47. http://dx.doi.org/10.1016/j.future.2015.11.010
  • Nancy Noella, R. S., Gupta, D., & Priyadarshini, J. (2019). Diagnosis of Parkinson’s disease using Gait dynamics and images. Procedia Computer Science, 165, 428–434. https://doi.org/10.1016/j.procs.2020.01.002
  • Ortells, J., Herrero-Ezquerro, M. T., & Mollineda, R. A. (2018). Vision-based gait impairment analysis for aided diagnosis. Medical & Biological Engineering & Computing, 56(9), 1553–1564. https://doi.org/10.1007/s11517-018-1795-2
  • Pourghayoomi, E., Behzadipour, S., Ramezani, M., Joghataei, M. T., & Shahidi, G. A. (2020). A new postural stability‑indicator to predict the level of fear of falling in Parkinson’s disease patients. BioMedical Engineering OnLine, 19(64), 1–18.
  • Rajavel, R., Ravichandran, S. K., Harimoorthy, K., Nagappan, P., & Gobichettipalayam, K. R. (2022). IoT-based smart healthcare video surveillance system using edge computing. Journal of Ambient Intelligence and Humanized Computing, 13(6), 3195–3207. https://doi.org/10.1007/s12652-021-03157-1
  • Rajavel, R., & Thangarathanam, M. (2016). Adaptive probabilistic behavioural learning system for the effective behavioural decision in cloud trading negotiation market. Future Generation Computer Systems, 58, 29–41. https://doi.org/10.1016/j.future.2015.12.007
  • Rajavel, R., & Thangarathanam, M. (2021). Agent-based automated dynamic SLA negotiation framework in the cloud using the stochastic optimization approach. Applied Soft Computing, 101, 107040. https://doi.org/10.1016/j.asoc.2020.107040
  • Rodriguez-Martin, D., Samà, A., Perez-Lopez, C., Català, A., Cabestany, J., & Rodriguez-Molinero, A. (2013). SVM-based posture identification with a single waist-located triaxial accelerometer. Expert Systems with Applications, 40(18), 7203–7211.
  • Sonawane, B., & Sharma, P. (2021). Review of automated emotion-based quantification of facial expression in Parkinson’s patients. The Visual Computer, 37(5), 1151–1167. https://doi.org/10.1007/s00371-020-01859-9
  • Vermilyea, S. C., & Emborg, M. E. (2018). The role of nonhuman primate models in the development of cell based therapies for Parkinson’s disease. Journal of Neural Transmission, 125(3), 365–384. https://doi.org/10.1007/s00702-017-1708-9
  • Waldthaler, J., Krüger-Zechlin, C., Stock, L., Deeb, Z., & Timmermann, L. (2019). New insights into facial emotion recognition in Parkinson’s disease with and without mild cognitive impairment from visual scanning patterns. Clinical Parkinsonism & Related Disorders, 1, 102–108. http://dx.doi.org/10.1016/j.prdoa.2019.11.003
  • Xu, S., & Pan, Z. (2020). A novel ensemble of random forest for assisting diagnosis of Parkinson’s Disease on small handwritten dynamics dataset. International Journal of Medical Informatics, 144, 104283. https://doi.org/10.1016/j.ijmedinf.2020.104283

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.