A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection

Emin Ölmez; Orhan Akbulut; Ahmet Sertbaş

Makale detayları

A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection

Yazarlar:

Emin Ölmez | Orhan Akbulut | Ahmet Sertbaş

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 1, pp.35-43

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290339

pISSN:

2667-484X

Anahtar kelimeler:

Özet:

 Thanks to the developing technology, Parkinsons disease can be detected by using datasets which are obtained from different sources. Gait activity analysis is one of the methods used to detect Parkinsons disease. The gait activity of Parkinsons disease differs from the gait of a normal person. In this study, a support vector machine-based classification method using low-dimensional feature vector representation is proposed to detect Parkinsons disease. Pressure sensors placed under the foot are divided into 3 categories, placed on the heel of the foot, the center of the foot, and the toe. Average stance duration, average stride duration, and average distance are extracted from the heel of the foot and toe. The frequency value obtained from the center of the foot during the walking period is used. Only 4 feature values having On time complexity are used for the classification process. Experimental results point out that the proposed method can compete with similar studies proposed in the literature, even under these few features. According to the experimental results, high classification performance, up to 85, is obtained under the whole dataset. Moreover, superior classification performance, up to 91, is obtained when the datasets are evaluated individually.

 Thanks to the developing technology, Parkinsons disease can be detected by using datasets which are obtained from different sources. Gait activity analysis is one of the methods used to detect Parkinsons disease. The gait activity of Parkinsons disease differs from the gait of a normal person. In this study, a support vector machine-based classification method using low-dimensional feature vector representation is proposed to detect Parkinsons disease. Pressure sensors placed under the foot are divided into 3 categories, placed on the heel of the foot, the center of the foot, and the toe. Average stance duration, average stride duration, and average distance are extracted from the heel of the foot and toe. The frequency value obtained from the center of the foot during the walking period is used. Only 4 feature values having On time complexity are used for the classification process. Experimental results point out that the proposed method can compete with similar studies proposed in the literature, even under these few features. According to the experimental results, high classification performance, up to 85, is obtained under the whole dataset. Moreover, superior classification performance, up to 91, is obtained when the datasets are evaluated individually.

 Thanks to the developing technology, Parkinsons disease can be detected by using datasets which are obtained from different sources. Gait activity analysis is one of the methods used to detect Parkinsons disease. The gait activity of Parkinsons disease differs from the gait of a normal person. In this study, a support vector machine-based classification method using low-dimensional feature vector representation is proposed to detect Parkinsons disease. Pressure sensors placed under the foot are divided into 3 categories, placed on the heel of the foot, the center of the foot, and the toe. Average stance duration, average stride duration, and average distance are extracted from the heel of the foot and toe. The frequency value obtained from the center of the foot during the walking period is used. Only 4 feature values having On time complexity are used for the classification process. Experimental results point out that the proposed method can compete with similar studies proposed in the literature, even under these few features. According to the experimental results, high classification performance, up to 85, is obtained under the whole dataset. Moreover, superior classification performance, up to 91, is obtained when the datasets are evaluated individually.

 Thanks to the developing technology, Parkinsons disease can be detected by using datasets which are obtained from different sources. Gait activity analysis is one of the methods used to detect Parkinsons disease. The gait activity of Parkinsons disease differs from the gait of a normal person. In this study, a support vector machine-based classification method using low-dimensional feature vector representation is proposed to detect Parkinsons disease. Pressure sensors placed under the foot are divided into 3 categories, placed on the heel of the foot, the center of the foot, and the toe. Average stance duration, average stride duration, and average distance are extracted from the heel of the foot and toe. The frequency value obtained from the center of the foot during the walking period is used. Only 4 feature values having On time complexity are used for the classification process. Experimental results point out that the proposed method can compete with similar studies proposed in the literature, even under these few features. According to the experimental results, high classification performance, up to 85, is obtained under the whole dataset. Moreover, superior classification performance, up to 91, is obtained when the datasets are evaluated individually.

Makale detayları

A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection

Yazarlar:

Emin Ölmez | Orhan Akbulut | Ahmet Sertbaş

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 1, pp.35-43

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290339

pISSN:

2667-484X

Classification

Gait Analysis

Parkinson

Support Vector Machine

Sayı

Makale detayları

A Low-Dimensional Feature Vector Representation for Gait-based Parkinson’s Disease Detection

Yazarlar: Emin Ölmez | Orhan Akbulut | Ahmet Sertbaş

Yayıncı: Kocaeli University

Ana Başlıklar: Yapay Zeka

Dergi: Kocaeli Journal of Science and Engineering Vol.6 Issue 1, pp.35-43

Belge Tipi: Makale

Yayın Tarihi: 5/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001290339

pISSN: 2667-484X

Classification

Gait Analysis

Parkinson

Support Vector Machine

Sayı

Yazarlar:

Emin Ölmez | Orhan Akbulut | Ahmet Sertbaş

Yayıncı:

Kocaeli University

Ana Başlıklar:

Yapay Zeka

Dergi:

Kocaeli Journal of Science and Engineering Vol.6 Issue 1, pp.35-43

Belge Tipi:

Makale

Yayın Tarihi:

5/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001290339

pISSN:

2667-484X