Vision-Based Gait Analysis for Neurodegenerative Disorders Detection
Article Sidebar
Main Article Content
Abstract
Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A dataset containing gait videos from normal subjects and PD patients were collected, along with a control group of 25 healthy adults. The participants were requested to perform a Timed Up and Go (TUG) test, during which their walking sequences were recorded using two smartphones positioned at different angles, namely side and front. A multi-person pose estimator is used to estimate human skeletal joint points from the collected gait videos. Different gait features associated with PD including stride length, number of steps taken during turn, turning duration, speed and cadence are derived from these key point information to perform PD detection. Experimental results show that the proposed solution achieves an accuracy of 89.39%. The study's findings demonstrate the potential of markerless vision-based gait acquisition techniques for early detection of PD symptoms.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
S. Chaudhary & N. Tyagi (2022). A Review on Parkinson’s Disease: Overview and Management. International Journal of Pharmaceutical Sciences Review and Research, 18–24. https://doi.org/10.47583/ijpsrr.2022.v76i01.004
B. Yvette (2023, April 4). Parkinson’s disease: Early signs, causes, and risk factors. https://www.medicalnewstoday.com/articles/323396
J. Parkinson (1817). An Essay on the Shaking Palsy. In J Neuropsychiatry Clin Neurosci (Vol. 14, Issue 2).
S. Soltaninejad, A. Rosales-Castellanos, M. A. Ibarra-Manzano & L. Cheng (2018). Body Movement Monitoring for Parkinson’s Disease Patients Using A Smart Sensor Based Non-Invasive Technique. 2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom), 1–6. https://doi.org/10.1109/HealthCom.2018.8531197
K. Polat (2019). Freezing of Gait (FoG) Detection Using Logistic Regression in Parkinson’s Disease from Acceleration Signals. 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), 1–4. https://doi.org/10.1109/EBBT.2019.8742042
K. Devi Das, A. J. Saji & C. S. Kumar (2017). Frequency analysis of gait signals for detection of neurodegenerative diseases. 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT), 1–6. https://doi.org/10.1109/ICCPCT.2017.8074273
C. W. Cho, W.H. Chao, S.H. Lin & Y.Y. Chen (2009). A vision-based analysis system for gait recognition in patients with Parkinson’s disease. Expert Systems with Applications, 36(3), 7033–7039. https://doi.org/10.1016/j.eswa.2008.08.076
S. Shetty & Y. S. Rao (2016). SVM based machine learning approach to identify Parkinson’s disease using gait analysis. 2016 International Conference on Inventive Computation Technologies (ICICT), 1–5. https://doi.org/10.1109/INVENTIVE.2016.7824836
M. N. Alam, A. Garg, T. T. K. Munia, R. Fazel-Rezai & K. Tavakolian (2017). Vertical ground reaction force marker for Parkinson’s disease. PLOS ONE, 12(5), e0175951. https://doi.org/10.1371/journal.pone.0175951
D. Buongiorno, I. Bortone, G. D. Cascarano, G. F. Trotta, A. Brunetti & V. Bevilacqua (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(S9), 243. https://doi.org/10.1186/s12911-019-0987-5
D. Trabassi, M. Serrao, T. Varrecchia, A. Ranavolo, G. Coppola, R. De Icco, C. Tassorelli & S. F. Castiglia (2022). Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis. Sensors, 22(10), 3700. https://doi.org/10.3390/s22103700
M. Shaban (2021). Automated Screening of Parkinson’s Disease Using Deep Learning Based Electroencephalography. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 158–161. https://doi.org/10.1109/NER49283.2021.9441065
M. Sivakumar, A. H. Christinal & S. Jebasingh (2021). Parkinson’s disease Diagnosis using a Combined Deep Learning Approach. 2021 3rd International Conference on Signal Processing and Communication (ICPSC), 81–84. https://doi.org/10.1109/ICSPC51351.2021.9451719
S. S. Upadhya & A. N. Cheeran (2018). Discriminating Parkinson and Healthy People Using Phonation and Cepstral Features of Speech. Procedia Computer Science, 143, 197–202. https://doi.org/10.1016/j.procs.2018.10.376
A. H. Butt, F. Cavallo, C. Maremmani, & E. Rovini (2020). Biomechanical parameters assessment for the classification of Parkinson Disease using Bidirectional Long Short-Term Memory. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 5761–5764. https://doi.org/10.1109/EMBC44109.2020.9176051
R. Kaur, R. W. Motl, R. Sowers & M. E. Hernandez (2022). A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson’s Disease Gait Dysfunctions—A Deep Learning Approach. IEEE Journal of Biomedical and Health Informatics, 27(1), 190–201. https://doi.org/10.1109/JBHI.2022.3208077
T. Asuroglu & H. Ogul (2022). A deep learning approach for parkinson’s disease severity assessment. Health and Technology, 12(5), 943–953. https://doi.org/10.1007/s12553-022-00698-z
R. Atri, K. Urban, B. Marebwa, T. Simuni, C. Tanner, A. Siderowf, M. Frasier, M. Haas & L. Lancashire (2022). Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors. Sensors, 22(18), 6831. https://doi.org/10.3390/s22186831
D. Podsiadlo & S. Richardson (1991). The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons. Journal of the American Geriatrics Society, 39(2), 142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x
R. Baker (2006). Gait analysis methods in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 3. https://doi.org/10.1186/1743-0003-3-4
S. L. Colyer, M. Evans, D. P. Cosker & A. I. T. Salo (2018). A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. In Sports Medicine - Open (Vol. 4, Issue 1). Springer. https://doi.org/10.1186/s40798-018-0139-y
P. Y. Chou & S. C. Lee (2013). Turning deficits in people with Parkinson’s disease. In Tzu Chi Medical Journal (Vol. 25, Issue 4, pp. 200–202). https://doi.org/10.1016/j.tcmj.2013.06.003
J. Das, R. Vitorio, A. Butterfield, R. Morris, L. Graham, G. Barry, C. McDonald, R. Walker, M. Mancini & S. Stuart (2022). Visual Cues for Turning in Parkinson’s Disease. Sensors, 22(18). https://doi.org/10.3390/s22186746
M. Mancini, A. Weiss, T. Herman & J. M. Hausdorff (2018). Turn around freezing: Community-living turning behavior in people with Parkinson’s disease. Frontiers in Neurology, 9(JAN). https://doi.org/10.3389/fneur.2018.00018
Y. F. Ti, T. Connie, M.K.O. Goh. (2023). GenReGait: Gender Recognition using Gait Features. Journal of Informatics and Web Engineering, 2(2). https://doi.org/10.33093/jiwe.2023.2.2.10