Framework for early detection and classification of balance pathologies using posturography and anthropometric variables.
Early detection of balance-related pathologies in adults using Posturography, anthropometric and personal data is limited. Our goal is to address this issue. It will enable us to identify adults in early stages of balance disorders using easily accessible and measurable data. Open-source data of 163...
| Publicado en: | Clinical Biomechanics Vol. 113 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176066697&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176066697 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02680033 JB1 jtl: Clinical Biomechanics issn: 02680033 maglogo: N pubinfo: dt: Mar2024 vid: 113 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 176066697 176066697 176066697 10.1016/j.clinbiomech.2024.106214 176066697 ppct: 1 formats: tig: atl: Framework for early detection and classification of balance pathologies using posturography and anthropometric variables. aug: au: Sarmah, Arnab Aggarwal, Raghav Vitekar, Sarth Sameer Katao, Shunsuke Boruah, Lipika Ito, Satoshi Kanagaraj, Subramani affil: Department of Mechanical Engineering, IIT Guwahati, Assam, India sug: subj: Balance, Postural Posturography Anthropometry Early Diagnosis Human Male Female Descriptive Statistics Instrument by Type Male Female ab: Early detection of balance-related pathologies in adults using Posturography, anthropometric and personal data is limited. Our goal is to address this issue. It will enable us to identify adults in early stages of balance disorders using easily accessible and measurable data. Open-source data of 163 subjects (47 males and 116 females) is used to train and test classification algorithms. Features include mean and standard deviation of the center of pressure displacement, obtained through posturography, the anthropometric and personal variables (age, sex, body mass index, foot length), and Trail Making Test scores. 75% of the data is employed for training and 25% of the data is used for testing. It is then validated using an indigenously collected dataset of healthy individuals. Accuracy and Sensitivity, both, increases when anthropometric and personal variables are included alongside center of pressure features for classification. Specificity decreases slightly with the addition of anthropometric and personal variables with center of pressure displacement feature, which also affects the classification algorithms' performance. Standard deviation of the center of pressure displacement is found to be more effective than the mean value. A similar trend of the increased performance is observed during validation, except when neural networks were used for the classification. Posturography data, Anthropometric measurements, personal data and self-assessment scales can identify balance issues in adults, making it suitable for community health centers with limited resources. Early detection prompts timely medical care, improving the management of disorders and thus enhancing the quality of life through rehabilitation. • Identification of Individuals with balance issues using easy to collect information. • Applicable through community health centers. • Improves Quality of Life of affected individuals through early identification and intervention. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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