Cover Image
QR Code

Prediction accuracy in estimating joint angle trajectories using a video posture coding method for sagittal lifting tasks.

This study investigated prediction accuracy of a video posture coding method for lifting joint trajectory estimation. From three filming angles, the coder selected four key snapshots, identified joint angles and then a prediction program estimated the joint trajectories over the course of a lift. Re...

Full description

Published in:Ergonomics Vol. 53; no. 8; pp. 1039 - 1048
Main Authors: Chang C, McGorry RW, Xu X, Hsiang SM
Format: pictorial research tables/charts Journal Article
Published: Taylor & Francis Ltd Aug2010
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105068743&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 105068743
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        00140139
        ERO
      jtl: Ergonomics
      issn: 00140139
      maglogo: Y
    pubinfo:
      dt: Aug2010
      vid: 53
      iid: 8
      pid: 377
      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
    artinfo:
      ui:
        105068743
        2010731972
        10.1080/00140139.2010.489963
        NLM20658398
        105068743
      ppf: 1039
      ppct: 9
      formats:
      tig:
        atl: Prediction accuracy in estimating joint angle trajectories using a video posture coding method for sagittal lifting tasks.
      aug:
        au:
          Chang C
          McGorry RW
          Xu X
          Hsiang SM
        affil: Liberty Mutual Research Institute for Safety, 71 Frankland Road, Hopkinton, MA 01748, USA
      sug:
        subj:
          Lifting
          Posture
          Simulations
          Analysis of Variance
          Biomechanics
          Ergonomics
          Human
          Interrater Reliability
          Multivariate Analysis of Variance
          Post Hoc Analysis
          Surveys
          Videorecording
      ab: This study investigated prediction accuracy of a video posture coding method for lifting joint trajectory estimation. From three filming angles, the coder selected four key snapshots, identified joint angles and then a prediction program estimated the joint trajectories over the course of a lift. Results revealed a limited range of differences of joint angles (elbow, shoulder, hip, knee, ankle) between the manual coding method and the electromagnetic motion tracking system approach. Lifting range significantly affected estimate accuracy for all joints and camcorder filming angle had a significant effect on all joints but the hip. Joint trajectory predictions were more accurate for knuckle-to-shoulder lifts than for floor-to-shoulder or floor-to-knuckle lifts with average root mean square errors (RMSE) of 8.65°, 11.15° and 11.93°, respectively. Accuracy was also greater for the filming angles orthogonal to the participant's sagittal plane (RMSE = 9.97°) as compared to filming angles of 45° (RMSE = 11.01°) or 135° (10.71°). The effects of lifting speed and loading conditions were minimal. To further increase prediction accuracy, improved prediction algorithms and/or better posture matching methods should be investigated. Statement of Relevance: Observation and classification of postures are common steps in risk assessment of manual materials handling tasks. The ability to accurately predict lifting patterns through video coding can provide ergonomists with greater resolution in characterising or assessing the lifting tasks than evaluation based solely on sampling with a single lifting posture event.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N