Evaluation of scoliosis using baropodometer and artificial neural network
| Autor: | Marcelo Augusto Assunção Sanches, Aparecido Augusto de Carvalho, Mateus Fernandes Réu Urban, Caroline Meireles Fanfoni, Erica Regina Marani Daruichi Machado, Fabian Castro Forero |
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| Přispěvatelé: | Universidade Estadual Paulista (Unesp), Federal Institute of São Paulo |
| Jazyk: | angličtina |
| Předmět: |
medicine.medical_specialty
lcsh:Biotechnology 0206 medical engineering Biomedical Engineering Single layer perceptron 02 engineering and technology Scoliosis 01 natural sciences Physical medicine and rehabilitation lcsh:TP248.13-248.65 medicine Baropodometer Electronic systems lcsh:R5-920 Artificial neural network Artificial neural networks business.industry 010401 analytical chemistry medicine.disease Perceptron 020601 biomedical engineering Spinal column Backpropagation 0104 chemical sciences Coronal plane business lcsh:Medicine (General) Single layer Weight discharge |
| Zdroj: | Research on Biomedical Engineering, Vol 33, Iss 2, Pp 121-129 SciELO Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP Research on Biomedical Engineering v.33 n.2 2017 Research on Biomedical Engineering Sociedade Brasileira de Engenharia Biomédica (SBEB) instacron:SBEB Research on Biomedical Engineering, Volume: 33, Issue: 2, Pages: 121-129, Published: JUN 2017 |
| ISSN: | 2446-4740 |
| Popis: | Introduction: One of the most recurrent pathologies in the spine is scoliosis. It occurs in the frontal plane and is formed by one or more curves in the spinal column. The scoliosis causes global postural misalignment in an individual. One of the modifications produced by postural misalignment is the way in which an individual distributes weight to the feet. We aimed to implement an electronic system for separating patients with Degree I scoliosis (i.e., 1° to 19° scoliosis according to the Ricard classification) into two groups: C1 (1°-9°) and C2 (10°-9°). The highest percentage of patients with scoliosis is in this range: those who do not need to wear vests or undergo surgery and whose treatment is performed via special physical exercise and frequent evaluations by healthcare professionals. Methods The electronic system consists of a baropodometer and artificial neural networks (ANNs). The classification of patients in the scoliosis groups was performed with MATLAB software and a Single Layer Perceptron network using the backpropagation training algorithm. Evaluations were performed on 63 volunteers. Results The mean classification sensitivity was 93.7% in the C1 group and 94.5% in the C2 group. The classification accuracy was 83.3% in the C1 group and 96.0% in the C2 group. Conclusion The implemented system can contribute to the treatment of patients with scoliosis grades ranging from 1° to 19°, which represents the highest incidence of this pathology, for which the monitoring of the clinical condition using noninvasive techniques is of fundamental importance. |
| Databáze: | OpenAIRE |
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