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
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.
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.
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.