Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras.
Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect)...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 2; pp. 373 - 383 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=141513922&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141513922 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2020 vid: 58 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141513922 141513922 NLM31853775 141513922 10.1007/s11517-019-02079-7 NLM31853775 141513922 ppf: 373 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras. aug: au: Luo, Guoliang Zhu, Yean Wang, Rui Tong, Yang Lu, Wei Wang, Haolun affil: East China Jiaotong University, Nanchang, China sug: subj: Photography Economics Gait Disorders, Neurologic Algorithms Costs and Cost Analysis Gait Disorders, Neurologic Diagnosis Hemiplegia Photography Equipment and Supplies Hemiplegia Physiopathology Hemiplegia Economics Middle Age ROC Curve Female Male Scales Human Middle Aged: 45-64 years Female Male ab: Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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