Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation
Intermittent claudication is a walking symptom. Patients with intermittent claudication experience lower limb pain after walking for a short time. However, rest relieves the pain and allows the patient to walk again. Unfortunately, this symptom predominantly arises from not 1 but 2 different disease...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/861529 |
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| author | Tetsuyou Watanabe Takeshi Yoneyama Hiroyuki Hayashi Yasumitsu Toribatake |
| author_facet | Tetsuyou Watanabe Takeshi Yoneyama Hiroyuki Hayashi Yasumitsu Toribatake |
| author_sort | Tetsuyou Watanabe |
| collection | DOAJ |
| description | Intermittent claudication is a walking symptom. Patients with intermittent claudication experience lower limb pain after walking for a short time. However, rest relieves the pain and allows the patient to walk again. Unfortunately, this symptom predominantly arises from not 1 but 2 different diseases: LSS (lumber spinal canal stenosis) and PAD (peripheral arterial disease). Patients with LSS can be subdivided by the affected vertebra into 2 main groups: L4 and L5. It is clinically very important to determine whether patients with intermittent claudication suffer from PAD, L4, or L5. This paper presents a novel SVM- (support vector machine-) based methodology for such discrimination/differentiation using minimally required data, simple walking motion data in the sagittal plane. We constructed a simple walking measurement system that is easy to set up and calibrate and suitable for use by nonspecialists in small spaces. We analyzed the obtained gait patterns and derived input parameters for SVM that are also visually detectable and medically meaningful/consistent differentiation features. We present a differentiation methodology utilizing an SVM classifier. Leave-one-out cross-validation of differentiation/classification by this method yielded a total accuracy of 83%. |
| format | Article |
| id | doaj-art-0aed00356e2a47599e2acd2446cc16a0 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0aed00356e2a47599e2acd2446cc16a02025-02-03T01:11:24ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/861529861529Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and DifferentiationTetsuyou Watanabe0Takeshi Yoneyama1Hiroyuki Hayashi2Yasumitsu Toribatake3The School of Mechanical Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, JapanThe School of Mechanical Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, JapanDepartment of Orthopedic Surgery, Graduate School of Medical Science, Kanazawa University, JapanDepartment of Orthopedic Surgery, Koseiren Takaoka Hospital, JapanIntermittent claudication is a walking symptom. Patients with intermittent claudication experience lower limb pain after walking for a short time. However, rest relieves the pain and allows the patient to walk again. Unfortunately, this symptom predominantly arises from not 1 but 2 different diseases: LSS (lumber spinal canal stenosis) and PAD (peripheral arterial disease). Patients with LSS can be subdivided by the affected vertebra into 2 main groups: L4 and L5. It is clinically very important to determine whether patients with intermittent claudication suffer from PAD, L4, or L5. This paper presents a novel SVM- (support vector machine-) based methodology for such discrimination/differentiation using minimally required data, simple walking motion data in the sagittal plane. We constructed a simple walking measurement system that is easy to set up and calibrate and suitable for use by nonspecialists in small spaces. We analyzed the obtained gait patterns and derived input parameters for SVM that are also visually detectable and medically meaningful/consistent differentiation features. We present a differentiation methodology utilizing an SVM classifier. Leave-one-out cross-validation of differentiation/classification by this method yielded a total accuracy of 83%.http://dx.doi.org/10.1155/2014/861529 |
| spellingShingle | Tetsuyou Watanabe Takeshi Yoneyama Hiroyuki Hayashi Yasumitsu Toribatake Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation The Scientific World Journal |
| title | Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation |
| title_full | Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation |
| title_fullStr | Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation |
| title_full_unstemmed | Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation |
| title_short | Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation |
| title_sort | identification of the causative disease of intermittent claudication through walking motion analysis feature analysis and differentiation |
| url | http://dx.doi.org/10.1155/2014/861529 |
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