Posture prediction for static sagittal-plane lifting

Marc J. Dysart, Jeffrey C. Woldstad

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Three separate models are presented to predict the postures of humans performing static sagittal lifting tasks. The models use a common inverse-kinematics characterization to represent mathematically feasible postures, but explore different criteria functions for selecting a final posture. The first criterion assumes that subjects minimize the overall effort associated with a posture. The second criterion expresses effort locally as opposed to globally, and minimizes this value. The third criterion maximizes body stability. The postures predicted by these three models were compared to the postures assumed by 16 subjects performing 4 static sagittal lifting tasks. The results showed that all of the prediction errors were significantly greater than zero, but that the first objective function (minimum total torque) was more accurate than the other two criteria. The models were in general less accurate for postures that had lower hand positions than for those with higher hand positions.

Original languageEnglish (US)
Pages (from-to)1393-1397
Number of pages5
JournalJournal of Biomechanics
Volume29
Issue number10
DOIs
StatePublished - Oct 1996

Keywords

  • Inverse kinematics
  • Lifting
  • Nonlinear programming
  • Posture prediction

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Biomedical Engineering
  • Rehabilitation

Fingerprint

Dive into the research topics of 'Posture prediction for static sagittal-plane lifting'. Together they form a unique fingerprint.

Cite this