Selection of clinical features for pattern recognition applied to gait analysis.
This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 4; pp. 685 - 696 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Apr2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121883616&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121883616 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2017 vid: 55 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121883616 121883616 NLM27435068 10.1007/s11517-016-1546-1 NLM27435068 121883616 ppf: 685 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Selection of clinical features for pattern recognition applied to gait analysis. aug: au: Altilio, Rosa Paoloni, Marco Panella, Massimo affil: Department of Information Engineering, Electronics and Telecommunications (DIET) , University of Rome 'La Sapienza' , Via Eudossiana, 18 00184 Rome Italy sug: subj: Information Science Methods Gait Physiology Monitoring, Physiologic Methods Aged Adult Case Control Studies Osteoarthritis, Hip Physiopathology Middle Age Parkinson Disease Physiopathology Aged, 80 and Over Multiple Sclerosis Physiopathology Female Probability Young Adult Male Hemiplegia Physiopathology Reproducibility of Results Scales Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years Aged, 80 & over Female Male ab: This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadence and stride may add useful information for the movement evaluation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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