Research Article
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Year 2024, Volume: 37 Issue: 1, 5 - 10, 28.01.2024

Hazal Taş Ahmet Yardımcı Hilmi Uysal Uğur Bilge

https://doi.org/10.5472/marumj.1379890

Abstract

References

  • Jørgensen H S, Nakayama H, Raaschou H O, Olsen T S. Recovery of walking function in stroke patients: The copenhagen stroke study. Arch Phys Med Rehabil 2014; 76:27- 32. doi:10.1016/S0003-9993(95)80038-7.
  • Kuan TS, Tsou JY, Su FC. Hemiplegic gait of stroke patients: The effect of using a cane. Arch Phys Med Rehabil 1999; 80: 777-84. doi: 10.1016/s0003-9993(99)90227-7.
  • Van Sant A F. Movement System Diagnosis. J Neurol Physl Ther 2017; 41: 10-6. doi: 10.1097/NPT.000.000.0000000152.
  • Guo Y, Wu D, Liu G, Zhao G, Huang B, Wang L. A low-cost body inertial-sensing network for practical gait discrimination of hemiplegia patients. Telemed J E Health 2012; 18: 748-54. doi: 10.1089/tmj.2012.0014.
  • Muro-de-la-Herran A, Garcia Zapirain B, Mendez-Zorrilla A. Gait analysis methods: An overview of wearable and nonwearable systems, highlighting clinical Applications. Sensors (Basel) 2014; 14: 3362-94. doi: 10.3390/s140203362.
  • Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012; 12: 2255-83. doi: 10.3390/ s120202255.
  • Meyer Y. Wavelets: Algorithms and applications. Philadelphia: Society for Industrial and Applied Mathematics, 1993: 133.
  • Samant A. Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput‐Aided Civ Infrastruct Eng 2003; 15: 241-50. doi: 10.1111/0885-9507.00188.
  • Lee J, Park S, Shin H. Detection of hemiplegic walking using a wearable inertia sensing device. Sensors (Basel) 2018; 18: 1736. doi: 10.3390/s18061736.
  • Li M, Tian S, Sun L, Chen X. Gait analysis for post-stroke hemiparetic patient by Multi-Features Fusion Method. Sensors (Basel) 2019; 19: 1737. doi: 10.3390/s19071737.
  • Sekine M, Abe Y, Sekimoto M, et al. Assessment of gait parameter in hemiplegic patients by accelerometry. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143). Chicago 2000; 3: 1879-82. doi: 10.1109/ IEMBS.2000.900456.
  • Yardımcı A. Fuzzy logic based gait classification for hemiplegic patients. Lect Notes Comput Sci 2007; 4723: 344-54.
  • Toprak IB. Analysis of EEG signals using the wavelet transform and artificial neural network [master’s thesis]. University of Süleyman Demirel, Isparta 2007.
  • Aydın F, Aslan Z. Yapay öğrenme yöntemleri ve dalgacık dönüşümü kullanılarak nörodejeneratif hastalıkların teşhisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2017; 32: 749-66. doi: 10.17341/gazimmfd.337621
  • Wang N, Ambikairajah E, Lovell N H, Celler B G. Accelerometry based classification of walking patterns using time-frequency analysis. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon 2007; 36: 4899-902.

Classification of hemiplegia through gait analysis and machine learning methods

Year 2024, Volume: 37 Issue: 1, 5 - 10, 28.01.2024

Hazal Taş Ahmet Yardımcı Hilmi Uysal Uğur Bilge

https://doi.org/10.5472/marumj.1379890

Abstract

Objective: Gait analysis is a method that is used for understanding normal walking and determining the stage of the disease as it affects
walking. It is important to objectively determine the stage of the disease in order to decide interventions and treatment strategies. This
study aims to determine the Brunnstrom Stage of the hemiplegic patients with an analysis of gait data.
Patients and Methods: In the first part of the study, the gait signal data were taken from 28 post-stroke hemiplegic patients and
7 healthy individuals with three-axis accelerometers. In the second part, new gait data were collected from 15 healthy individuals
through an accelerometer on the anteroposterior axis.
First the accelerometer signals were decomposed to Daubechies 5 (Db5) level six wavelets using MATLAB software. Subsequently,
these attributes were classified through several classifier and machine learning algorithms on WEKA and MATLAB software packages
to predict the stages of hemiplegia.
Results: The highest accuracy rate in the prediction of hemiplegia stage was achieved with the LogitBoost algorithm on WEKA with
91% for 35 samples, and 90% for 50 samples. This performance was followed by the RUSBoosted Trees algorithm on the MATLAB
software with an accuracy of 86.1% correct prediction.
Conclusion: The Brunnstrom Stage of hemiplegia can be predicted with machine learning algorithms with a good accuracy, helping
physicians to classify hemiplegic patients into correct stages, monitor and manage their rehabilitation.

Keywords

Hemiplegia Stroke Gait analysis Brunnstrom Machine learning

References

  • Jørgensen H S, Nakayama H, Raaschou H O, Olsen T S. Recovery of walking function in stroke patients: The copenhagen stroke study. Arch Phys Med Rehabil 2014; 76:27- 32. doi:10.1016/S0003-9993(95)80038-7.
  • Kuan TS, Tsou JY, Su FC. Hemiplegic gait of stroke patients: The effect of using a cane. Arch Phys Med Rehabil 1999; 80: 777-84. doi: 10.1016/s0003-9993(99)90227-7.
  • Van Sant A F. Movement System Diagnosis. J Neurol Physl Ther 2017; 41: 10-6. doi: 10.1097/NPT.000.000.0000000152.
  • Guo Y, Wu D, Liu G, Zhao G, Huang B, Wang L. A low-cost body inertial-sensing network for practical gait discrimination of hemiplegia patients. Telemed J E Health 2012; 18: 748-54. doi: 10.1089/tmj.2012.0014.
  • Muro-de-la-Herran A, Garcia Zapirain B, Mendez-Zorrilla A. Gait analysis methods: An overview of wearable and nonwearable systems, highlighting clinical Applications. Sensors (Basel) 2014; 14: 3362-94. doi: 10.3390/s140203362.
  • Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors (Basel) 2012; 12: 2255-83. doi: 10.3390/ s120202255.
  • Meyer Y. Wavelets: Algorithms and applications. Philadelphia: Society for Industrial and Applied Mathematics, 1993: 133.
  • Samant A. Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput‐Aided Civ Infrastruct Eng 2003; 15: 241-50. doi: 10.1111/0885-9507.00188.
  • Lee J, Park S, Shin H. Detection of hemiplegic walking using a wearable inertia sensing device. Sensors (Basel) 2018; 18: 1736. doi: 10.3390/s18061736.
  • Li M, Tian S, Sun L, Chen X. Gait analysis for post-stroke hemiparetic patient by Multi-Features Fusion Method. Sensors (Basel) 2019; 19: 1737. doi: 10.3390/s19071737.
  • Sekine M, Abe Y, Sekimoto M, et al. Assessment of gait parameter in hemiplegic patients by accelerometry. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143). Chicago 2000; 3: 1879-82. doi: 10.1109/ IEMBS.2000.900456.
  • Yardımcı A. Fuzzy logic based gait classification for hemiplegic patients. Lect Notes Comput Sci 2007; 4723: 344-54.
  • Toprak IB. Analysis of EEG signals using the wavelet transform and artificial neural network [master’s thesis]. University of Süleyman Demirel, Isparta 2007.
  • Aydın F, Aslan Z. Yapay öğrenme yöntemleri ve dalgacık dönüşümü kullanılarak nörodejeneratif hastalıkların teşhisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2017; 32: 749-66. doi: 10.17341/gazimmfd.337621
  • Wang N, Ambikairajah E, Lovell N H, Celler B G. Accelerometry based classification of walking patterns using time-frequency analysis. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon 2007; 36: 4899-902.
There are 15 citations in total.

Details

Primary Language English
Subjects Surgery (Other)
Journal Section Original Research
Authors

Hazal Taş 0000-0002-0872-7998

Ahmet Yardımcı 0000-0001-7241-4913

Hilmi Uysal 0000-0002-6063-377X

Uğur Bilge 0000-0002-5186-1092

Publication Date January 28, 2024
Published in Issue Year 2024 Volume: 37 Issue: 1

Cite

APA Taş, H., Yardımcı, A., Uysal, H., Bilge, U. (2024). Classification of hemiplegia through gait analysis and machine learning methods. Marmara Medical Journal, 37(1), 5-10. https://doi.org/10.5472/marumj.1379890
AMA Taş H, Yardımcı A, Uysal H, Bilge U. Classification of hemiplegia through gait analysis and machine learning methods. Marmara Med J. January 2024;37(1):5-10. doi:10.5472/marumj.1379890
Chicago Taş, Hazal, Ahmet Yardımcı, Hilmi Uysal, and Uğur Bilge. “Classification of Hemiplegia through Gait Analysis and Machine Learning Methods”. Marmara Medical Journal 37, no. 1 (January 2024): 5-10. https://doi.org/10.5472/marumj.1379890.
EndNote Taş H, Yardımcı A, Uysal H, Bilge U (January 1, 2024) Classification of hemiplegia through gait analysis and machine learning methods. Marmara Medical Journal 37 1 5–10.
IEEE H. Taş, A. Yardımcı, H. Uysal, and U. Bilge, “Classification of hemiplegia through gait analysis and machine learning methods”, Marmara Med J, vol. 37, no. 1, pp. 5–10, 2024, doi: 10.5472/marumj.1379890.
ISNAD Taş, Hazal et al. “Classification of Hemiplegia through Gait Analysis and Machine Learning Methods”. Marmara Medical Journal 37/1 (January 2024), 5-10. https://doi.org/10.5472/marumj.1379890.
JAMA Taş H, Yardımcı A, Uysal H, Bilge U. Classification of hemiplegia through gait analysis and machine learning methods. Marmara Med J. 2024;37:5–10.
MLA Taş, Hazal et al. “Classification of Hemiplegia through Gait Analysis and Machine Learning Methods”. Marmara Medical Journal, vol. 37, no. 1, 2024, pp. 5-10, doi:10.5472/marumj.1379890.
Vancouver Taş H, Yardımcı A, Uysal H, Bilge U. Classification of hemiplegia through gait analysis and machine learning methods. Marmara Med J. 2024;37(1):5-10.
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