Real-Time Neurological Disease Prediction with 3D Single Pose Estimation using MediaPipe
Keywords:regression, neurological, MediaPipe, estimate, circumstances, generalizability
For the diagnosis and analysis of chronic diseases including Cerebellar Ataxia (CA), Spinocerebellar Ataxia (SCA), and Parkinson's disease, artificial intelligence (AI) is an emerging field. Many doctors, diagnosis teams, and medical professionals benefit from the identification and analysis of neurological illnesses made possible by AI technologies like machine learning and deep learning. Our previous research was completed with the gait value analysis for foot position. This paper aims to conduct the research in real time prediction of neurological disease. For enhancing disease detection model performance and generalizability, diverse datasets from various populations and circumstances must be gathered and annotated. In this research, we applied MediaPipe for real-time disease recognition in videos. We gathered the gait values from the real time videos. The collected gait values are experimented with the target gait values of normal persons. It shows the difference of the gait analysis which helps to predict the disease. The single pose estimation is applied for the activities of human to analyse the gait values. It estimate the gait values in different ankles and compare with the target values. Our research exhibits the predicted result in the real time video of human actions. The experimental result provides the person is affected with neurological disease or a normal person with single pose estimation. Logistic regression algorithm is used to predict the accuracy of the disease. We got 98.53% accuracy for logistic regression algorithm.
Shanmuga Sundari, M., & Jadala, V. C. (2023). Neurological disease prediction using impaired gait analysis for foot position in cerebellar ataxia by ensemble approach. Automatika, 64(3), 541-550.
S. S. M, V. C. Jadala, S. K. Pasupuleti and P. Yellamma, "Deep Learning analysis using ResNet for Early Detection of Cerebellar Ataxia Disease," 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), Bhubaneswar, India, 2022, pp. 1-6, doi: 10.1109/ASSIC55218.2022.10088379.
Sundari, M. S., Jadala, V. C., & Pasupuleti, S. K. (2022, June). Prediction of Activity pattern mining for Neurological disease using Convolution Neural Network. In 2022 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 1319-1324). IEEE.
Chandra, J. V., & Pasupuleti, S. K. (2022, March). Machine Learning Methodologies for predicting Neurological disease using Behavioral Activity Mining in Health Care. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1035-1039). IEEE.
Raju, C. S. K., Pranitha, K., Samyuktha, P., & Madhumathi, J. (2022, March). Prediction of COVID 19-Chest Image Classification and Detection using RELM Classifier in Machine Learning. In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1184-1188). IEEE
Alsawadi, M. S., & Rio, M. (2022, August). Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks. In 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 206-211). IEEE.
Setiyadi, S., Mukhtar, H., Cahyadi, W. A., Lee, C. C., & Hong, W. T. (2022, December). Human Activity Detection Employing Full-Type 2D Blazepose Estimation with LSTM. In 2022 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) (pp. 1-7). IEEE.
Mroz, S., Baddour, N., McGuirk, C., Juneau, P., Tu, A., Cheung, K., & Lemaire, E. (2021, December). Comparing the quality of human pose estimation with blazepose or openpose. In 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART) (pp. 1-4). IEEE.
Min, Z. (2022, February). Human Body Pose Intelligent Estimation Based on BlazePose. In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) (pp. 150-153). IEEE.
Yang, H., Wang, Y., & Shi, Y. Rehabilitation Training Evaluation and Correction System Based on BlazePose.
Hakim, I. M., Zakaria, H., Muslim, K., & Ihsani, S. I. (2023, February). 3D Human Pose Estimation Using Blazepose and Direct Linear Transform (DLT) for Joint Angle Measurement. In 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 236-241). IEEE.
T. Ngo et al., “Balance deficits due to cerebellar ataxia: A machine learning and cloud-based approach,” IEEE Trans. Biomed. Eng., vol. 68,no. 5, pp. 1507–1517, May 2021.
A. Esteva et al., “A guide to deep learning in healthcare,” Nature Med., vol. 25, no. 1, pp. 24–29, Jan. 2019.
R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: Review, opportunities and challenges,” Briefings Bioinf., vol. 19, no. 6, pp. 1236–1246, Nov. 2018.
M. Adnan, S. Kalra, J. C. Cresswell, G. W. Taylor, and H. R. Tizhoosh, “Federated learning and differential privacy for medical image analysis,” Sci. Rep., vol. 12, no. 1, pp. 1–10, Dec. 2022.
M. Hao, H. Li, X. Luo, G. Xu, H. Yang, and S. Liu, “Efficient and privacy-enhanced federated learning for industrial artificial intelligence,” IEEE Trans. Ind. Informat., vol. 16, no. 10, pp. 6532–6542, Oct. 2020.
Y. Liu, J. J. Q. Yu, J. Kang, D. Niyato, and S. Zhang, “Privacy-preserving traffic flow prediction: A federated learning approach,” IEEE Internet Things J., vol. 7, no. 8, pp. 7751–7763, Aug. 2020.
Q. Wu, K. He, and X. Chen, “Personalized federated learning for intelligent IoT applications: A cloud-edge based framework,” IEEE Open J. Comput. Soc., vol. 1, pp. 35–44, 2020.
Y. Zhao, H. Liu, H. Li, P. Barnaghi, and H. Haddadi, “Semi-supervised federated learning for activity recognition,” 2020, arXiv:2011.00851.
Shanmuga Sundari, M., Sudha Rani, M., & Ram, K. B. (2023). Acute Leukemia Classification and Prediction in Blood Cells Using Convolution Neural Network. In International Conference on Innovative Computing and Communications (pp. 129-137). Springer, Singapore.
D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1622–1658, 3rd Quart., 2021.
Q. Wu, X. Chen, Z. Zhou, and J. Zhang, “FedHome: Cloud-edge based personalized federated learning for in-home health monitoring,” IEEE Trans. Mobile Comput., early access, Dec. 16, 2020, doi:10.1109/TMC.2020.3045266.
N. Rieke et al., “The future of digital health with federated learning,” npj Digit. Med., vol. 3, no. 1, pp. 1–7, Dec. 2020.
R. Kumar et al., “Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging,” IEEE Sensors J., vol. 21, no. 14, pp. 16301–16314, Jul. 2021.
S. Warnat-Herresthal et al., “Swarm learning for decentralized and confidential clinical machine learning,” Nature, vol. 594, no. 7862, pp. 265–270, 2021.
Sundari, M. S., & Nayak, R. K. (2020). Process mining in healthcare systems: a critical review and its future. International Journal of Emerging Trends in Engineering Research, 8(9).
Z. Yang, S. Zhong, A. Carass, S. H. Ying, and J. L. Prince, “Deep learning for cerebellar ataxia classification and functional score regression,” in Machine Learning in Medical Imaging, G. Wu, D. Zhang, and L. Zhou, Eds. Cham, Switzerland: Springer, 2014, pp. 68–76.
Z. Chang et al., “Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning,” Sci. Rep., vol. 10, no. 1, pp. 1–10, Dec. 2020.
B. Kashyap, P. N. Pathirana, M. Horne, L. Power, and D. Szmulewicz, “Quantitative assessment of speech in cerebellar ataxia using magnitude and phase based cepstrum,” Ann. Biomed. Eng., vol. 48, no. 4, pp. 1322–1336, Apr. 2020.
H. Tran, P. N. Pathirana, M. Horne, L. Power, and D. J. Szmulewicz, “Quantitative evaluation of cerebellar ataxia through automated assessment of upper limb movements,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 27, no. 5, pp. 1081–1091, May 2019.
Prof. Naveen Jain. (2013). FPGA Implementation of Hardware Architecture for H264/AV Codec Standards. International Journal of New Practices in Management and Engineering, 2(01), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/11
Ahammad, D. S. K. H. (2022). Microarray Cancer Classification with Stacked Classifier in Machine Learning Integrated Grid L1-Regulated Feature Selection. Machine Learning Applications in Engineering Education and Management, 2(1), 01–10. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/18
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.