Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions.
Objective: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift orig...
| Publicado en: | Human Factors Vol. 61; no. 1; pp. 64 - 78 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Feb2019
|
| 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=133802701&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133802701 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00187208 HMF jtl: Human Factors issn: 00187208 maglogo: Y pubinfo: dt: Feb2019 vid: 61 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 133802701 133802701 NLM30091947 133802701 10.1177/0018720818791367 NLM30091947 133802701 ppf: 64 ppct: 14 formats: tig: atl: Predicting Sagittal Plane Lifting Postures From Image Bounding Box Dimensions. aug: au: Greene, Runyu L. Hu, Yu Hen Difranco, Nicholas Wang, Xuan Lu, Ming-Lun Bao, Stephen Lin, Jia-Hua Radwin, Robert G. affil: University of Wisconsin-Madison, USA sug: subj: Posture Physiology Task Performance and Analysis Lifting Models, Anatomic Videorecording Decision Trees Algorithms Kinematics Human Reproducibility of Results Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Objective: A method for automatically classifying lifting postures from simple features in video recordings was developed and tested. We explored if an "elastic" rectangular bounding box, drawn tightly around the subject, can be used for classifying standing, stooping, and squatting at the lift origin and destination.Background: Current marker-less video tracking methods depend on a priori skeletal human models, which are prone to error from poor illumination, obstructions, and difficulty placing cameras in the field. Robust computer vision algorithms based on spatiotemporal features were previously applied for evaluating repetitive motion tasks, exertion frequency, and duty cycle.Methods: Mannequin poses were systematically generated using the Michigan 3DSSPP software for a wide range of hand locations and lifting postures. The stature-normalized height and width of a bounding box were measured in the sagittal plane and when rotated horizontally by 30°. After randomly ordering the data, a classification and regression tree algorithm was trained to classify the lifting postures.Results: The resulting tree had four levels and four splits, misclassifying 0.36% training-set cases. The algorithm was tested using 30 video clips of industrial lifting tasks, misclassifying 3.33% test-set cases. The sensitivity and specificity, respectively, were 100.0% and 100.0% for squatting, 90.0% and 100.0% for stooping, and 100.0% and 95.0% for standing.Conclusions: The tree classification algorithm is capable of classifying lifting postures based only on dimensions of bounding boxes.Applications: It is anticipated that this practical algorithm can be implemented on handheld devices such as a smartphone, making it readily accessible to practitioners. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|