A support vector machine approach to detect trans-tibial prosthetic misalignment using 3-Dimensional ground reaction force features: A proof of concept.
Background: Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience.Objective: In this paper, we explore the feasibility of using a support vector machine (SVM) approach to au...
| Publicado en: | Technology & Health Care Vol. 26; no. 4; pp. 715 - 722 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2018
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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=132074297&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132074297 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2018 vid: 26 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 132074297 132074297 NLM29991151 132074297 10.3233/THC-181338 NLM29991151 132074297 ppf: 715 ppct: 7 formats: tig: atl: A support vector machine approach to detect trans-tibial prosthetic misalignment using 3-Dimensional ground reaction force features: A proof of concept. aug: au: Zhang, Xueyi Liu, Zhicheng Fiedler, Goeran Cao, Zhe affil: School of Biomedical Engineering, Capital Medical University, Beijing, China sug: subj: Amputees Limb Prosthesis Prosthesis Design Tibia Pathology Aged Gait Middle Age Kinematics Male Adult Female Weight-Bearing Human Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Male Female ab: Background: Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience.Objective: In this paper, we explore the feasibility of using a support vector machine (SVM) approach to automatically detect misalignment of trans-tibial prostheses through ground reaction force (GRF).Methods: Alternate classification algorithms with varying kernels and feature sets were compared to assess the suitability for detection of a representative misalignment (six degrees of ankle plantar flexion) from normal alignment. A classical feature selection algorithm, Fisher Score, was further introduced to identify valuable features and reduce the dimension of feature sets.Results: The SVMs achieved a detection accuracy of 96.67% at best within the same subject and 88.89%, respectively, for inter-subject. Combined horizontal and vertical components of GRF features provided the maximum detection accuracies. Propulsion peak force was identified as key variable of gait for misalignment prediction.Conclusions: As a proof of concept, the results demonstrate potential in applying this approach to detect prosthetic misalignment based on gait patterns, and is a step towards future developments of tools for early prevention of misalignment in clinical. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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