Abnormal Gait Pattern Recognition of Stroke Patient in Initial Stage Using Smartphone and Hybrid Classification Methods
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Abstract
This paper proposes abnormal gait pattern recognition of Stroke patient in the initial stage using smartphone and hybrid classification methods. Our proposed research is divided into 2 parts: model building for abnormal gait pattern detection and system development. For a model of abnormal gait pattern detection, sensors (accelerometer and gyroscope sensor) on a smartphone are used to collect accelerometer and gyroscope data of gait pattern. Then, data of gait pattern are transformed and selected appropriated attributes to build a model using hybrid classification methods (Multilayer Perceptron, Decision Tree, and Support Vector Machine). From our experiments, the result shows that our proposed method achieved high accuracy of 99.40% for abnormal gait pattern recognition of Stroke patient in the initial stage and compared with previous research using accelerometer and gyroscope. Finally, our research developed a gait stroke detection system for abnormal gait pattern detection. When the system detects abnormal gait pattern, the system will send notification to caregiver and physician to reduce the risk of fall and accidence, especially, in the case of patient living alone at home. In addition, the system can help the physician to diagnose and train the gait pattern of post-stroke patients for rehabilitation.
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D. Tassanpoonchai, Stroke. Available Online at https://www.sikarin.com/content/detail/131/stroke, accessed on 11 May 2019.
World stroke Organization. Available Online at https://www.world-stroke.org/world-stroke-day-campaign/world-stroke-day/previous-world-stroke-days/world-stroke-day-2016, accessed on 18 April 2020.
Siriraj Stroke Center, Stroke. Available Online at https://www.si.mahidol.ac.th/th/healthdetail. asp?aid=1256, accessed on 25 January 2019.
H. Y. Wey, V. R. Desai, and T. Q. Duong, “A Review of Current Imaging Methods used in Stroke Research.” Neurological Research, Vol. 35, No. 10, pp. 1092-1102, December, 2013.
B. O. B. Erdene, and J. L. Saver, “Automatic Acute Stroke Symptom Detection and Emergency Medical Systems Alerting by Mobile Health Technologies: A Review.” Journal of Stroke and Cerebrovascular Diseases, Vol. 30, No. 7, pp.1-9, July, 2021.
S. Shahrestani, D. Wishart, S. M. J. Han, B. A. Strickland, J. Bakhsheshian, W. J. Mack, A.W. Toga, N. Sanossian, Y. C. Tai, and G. Zada, “A Systematic Review of Next-generation point-of-care Stroke Diagnostic Technologies.” Neurosurg Focus, Vol. 51, No. 1, pp. 1-12, July, 2021.
F. C. Wang, S. F. Chen, C. H. Lin, A. C. Lin, W. Yuan, Y.C. Li, and T. Y. Kuo, “Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units.” Sensors, Vol. 21, No. 1864, pp. 1-18, 2021.
M. Li, S. Tian, L. Sun and X. Chen, “Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method.” Sensors, Vol. 19, No. 1737, pp. 1-11, 2019.
Z. Huan, X. Chen, S. Lv, and H. Geng, “Gait Recognition of Acceleration Sensor for Smart Phone Based on Multiple Classifier Fusion.” Mathematical Problems in Engineering, Vol. 2019, pp. 1-18, 2019.
A. Mannini, D. Trojaniello, A. Cereatti, and A. M. Sabatini, “A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington's Diseases Patients.” Sensors, Vol.16, No.134, pp.1-14, January, 2016.
W. C. Hsu, T. Sugiarto, Y. Y. Liao, Y. J. Lin, F. C. Yang, D. Y. Hueng, C. T. Sun, and K. N. Chou, “Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Fearture?.” Applied Sciences, Vol.11, No. 1541, pp. 1-14, 2021.
S. Tiamkao, “Application for Save a Life: FAST TRACK.” North-Eastern Thai Journal of Neuroscience, Vol. 13, No. 3, pp. 57-69, 2018.
K. Snehasri, and N. V. Raghava Swamy, “Gait Detection in Stroke Patients is Based On a Wireless IOT System.” International Journal of Management, Technology and Engineering, Vol. 8, Issue IX, pp. 584-587, September, 2018.
S. Atnoor, and M. A. Waheed, “A Wireless IoT System Towards Gait Detection in Stroke Patients.” International Journal of Engineering Research & Technology, Vol. 9, No. 09, pp. 41-43, September, 2020.
S. Qui, Z. Wang, H. Zhao, L. Liu, and Y. Jiang, “Using Body-Worn Sensors for Preliminary Rehabilitation Assessment in Stroke Victims with Gait Impairment.” IEEE, Vol. 6, pp. 31249-31258, March, 2018.
C. C. Wei, S. W. Huang, S. L. Hsu, H. C. Chen, J. S. Chen, and H. Liang, “Analysis of using the Tongue Deviation Angle as a Warning Sign of a Stroke.” Biomedical Engineering, Vol. 11, No. 53, pp. 1-12, 2012.
M. Gagana, and M. C. Padma, “Review on Machine Learning Approaches used for Stroke Prediction.” International Research Journal of Engineering and Technology, Vol. 08, No. 3, pp. 805-809, March, 2021.
M.S. Sirsat, E. Ferme, and J. Camara, “Machine Learning for Brain Stroke: A Review.” Journal of Stroke and Cerebrovascular Diseases, Vol. 29, No. 10, pp. 1-17, October, 2020.
D. M. Mohan, A. H. Khandoker, S. A. Wasti, S. I. I. I. Alali, H. F. Jelinek, and K. Khalaf, “Assessment Methods of Post-Stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.” Frontiers in Neurology, Vol. 12, pp. 1-24, June, 2021.
J. Han, and M. Kamber. “Data Mining Concepts and Techniques.” Morgan Kaufmann Publishers, Third Edition, 2011.
S. Sinsomboonthong. “Data Mining 1: Discovering Knowledge in Data.” Chamchuree Products, Bangkok, Second Edition, 2017.
L. Hamel. “Knowledge Discovery with Support Vector Machines.” John Wiley & Sons, Inc, 2009.
S. Gammanee, and S. Chengseng, “Model for Predicting Success Factors in Agriculture in Kanchanaburi with Decision Trees.” Information Technology Journal, Vol. 16, No. 2, July- December, 2020.
S. Luangmaneerote, and A. Chaiwachiragompol, “Interesting Attributes of Student Performance Using Machine Learning Models Based on Family and Educational Backgrounds in the Faculty of Agriculture and Technology at Rajamangala University of Technology Isan.” Information Technology Journal, Vol. 17, No. 2, July- December, 2021.
K. Hengpraprohm, and S. Jungjit, “Missing Value Imputation Method using Ensemble Technique for Microarray Data.” Information Technology Journal, Vol. 14, No. 2, July-December, 2018.
S. Ankitha, M. Deepthi, N. Harshavardhan, M. Remanth, and V. Janhavi, “An Artificial Intelligence Approach to Predict Different Strokes.” International Journal of Engineering Research & Technology, Vol. 9, No.7, pp.1154-1157, July, 2020.
M. U. Emon, M. S. Keya, T. I. Meghla, M. Rahman, M.S. A. Mamun, and M. S. Kaiser, “Performance Analysis of Machine Learning Approaches in Stroke Prediction.” Proceedings of 2020 4th International Conference on Electronics, Communication and Aerospace Technology, India, pp. 1-6, 2021.
J. Yu, S. Park, S. H. Kwon, C. M. Benjamin, C. S. Pyo, and H. Lee, “AI-Based Stroke Disease Prediction System Using Real-Time Electromygraphy Signals.” Applied Sciences, Vol. 10, No. 6791, pp. 1-19, 2020.
H. Lee, E. J. Lee, S. Ham, H. B. Lee, J. S. Lee, S. U. Kwon, S.J. Kim, N. Kim, and D.W. Kang, “Machine Learning Approach to Identify Stroke within 4.5 Hours.” Stroke, Vol. 51, No. 3, pp. 860-866, March, 2020.
S. R. Priyanka, and M. Meera, “A Hybrid Feature Selection Method for Stroke Detection Using Machine Learning Approaches.” International Journal for Innovative Research in Science, Engineering and Technology V, ol. 9, No. 7, pp. 5653-5658, July, 2020.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A Public Domain Dataset for Human Activity Recognition Using Smartphones.” Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, pp. 437-442, 2013.
I. H. Khoo, P. Marayong, V. Krishnan, M. Balagtas, O. Rojas, and K. Leyba, “Real-time Biofeedback Device for Gait Rehabilitation of Post-Stroke Patients.” Biomedical Engineering Letters, Vol. 7, pp. 287-298, 2017.
W. Zhang, M. Smuck, C. Legault, M. A. Ith, A. Muaremi, and K. Aminian, “Gait Symmetry Assessment with a Low Back 3D Accelerometer in Post-Stroke Patients.” Sensors, Vol. 18, No. 3322, pp.1-12, October, 2018.
I. Hussian, and S. J. Park, “Prediction of Myoelectric Biomarkers in Post-Stroke Gait.” Sensors, Vol. 21, No. 5334, pp. 1-17, 2021.
Y. Wang, M. Mukaino, K. Ohtsuka, Y. Otaka, H. Tanikawa, F. Matsuda, K. Tsuchiyama, J. Yamada, and E. Saiton, “Gait Characteristics of Post-Stroke Hemiparetic Patients with Different Walking Speeds.” International Journal of Rehabilitation Research, Vol. 43, No. 1, pp. 69-75, March, 2020.
A. Schicketmueller, J. Lamprecht, M. Hofmann, M. Sailer, and G. Rose, “Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.” Sensors, Vol. 20, No. 3399, pp.1-12, June, 2020.
Class InfoGainAttributeEval, Available online at https://weka.sourceforge.io/doc.dev/weka/attribute Selection/InfoGainAttributeEval.html, accessed on 11 February 2020.
Class Ranker, Available online at https://weka.source forge.io/doc.dev/weka/attributeSelection/Ranker. html, accessed on 11 February 2020.
M. Whittle. “Gait analysis: an Introduction.” Philadelphia, Elsevier, Fourth Edition, 2006.
M.W. Whittle. “Gait Analysis: An Introduction.” Elsevier, Fourth Edition, 2007.