Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Marker-less systems for tracking working postures--results from two experiments.

S Pinzke1, L Kopp

  • 1Department of Agricultural Biosystems and Technology, Swedish University of Agricultural Sciences, Alnarp. Stefan.Pinzke@jbt.slu.se

Applied Ergonomics
|September 6, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fractures of the posterior malleolus as a part of complex ankle fractures: Trends in diagnosis and treatment at our institution.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti·2025
Same author

[Minimally Invasive Osteosynthesis of Calcaneal Fractures Using the Anterolateral Locking Plate].

Acta chirurgiae orthopaedicae et traumatologiae Cechoslovaca·2023
Same author

Calcaneal fractures - current trends and pitfalls.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti·2021
Same author

Acute and chronic Achilles tendon ruptures - current diagnostic and therapeutic options.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti·2021
Same author

Retrospective analysis of complications after treatment of acute Achilles tendon rupture by Kessler technique.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti·2021
Same author

Management of bone defects using the Masquelet technique of induced membrane.

Rozhledy v chirurgii : mesicnik Ceskoslovenske chirurgicke spolecnosti·2021
Same journal

Authority gradients in rail: A cross-jurisdictional systematic review and meta-synthesis.

Applied ergonomics·2026
Same journal

The distracting role of stress: Impaired executive attention and delayed fatigue perception.

Applied ergonomics·2026
Same journal

Analysis of work system components in interprofessional communication to determine shock etiology.

Applied ergonomics·2026
Same journal

Getting SSPOT to run: Development of a novel direct observational tool for usage in clinical settings.

Applied ergonomics·2026
Same journal

Investigating the impact of sopite syndrome on physiological responses during an immersive Augmented Reality (AR) game in a moving vehicle.

Applied ergonomics·2026
Same journal

The effect of aircraft cockpit rudder pedal shape on lower limb muscle activation, plantar pressure, and comfort.

Applied ergonomics·2026
See all related articles

Marker-less computer vision accurately classifies Ovako Working Posture Analyzing System (OWAS) postures. Image analysis and neural networks show promise for assessing workplace ergonomics and identifying harmful postures.

Area of Science:

  • Ergonomics and Occupational Health
  • Computer Vision and Image Analysis
  • Human-Computer Interaction

Background:

  • Assessing workplace postures is crucial for preventing musculoskeletal disorders.
  • Current methods for posture analysis can be labor-intensive and require specialized markers.
  • Marker-less approaches in computer vision offer a potential solution for automated posture assessment.

Purpose of the Study:

  • To evaluate the effectiveness of marker-less image analysis and computer vision techniques for automatic registration of Ovako Working Posture Analyzing System (OWAS) postures.
  • To compare a parametric image analysis method with a neural network approach for posture classification.

Main Methods:

  • Experiment 1: Developed a parametric image analysis method for subject segmentation and shape-based OWAS posture classification.

Related Experiment Videos

  • Experiment 2: Trained a computer neural network to classify OWAS postures from video data.
  • Both methods utilized marker-less video analysis.

    • Main Results:

    • The parametric image analysis method achieved 100% correct classification on 12 analyzed images.
    • The neural network, trained on 53 images, correctly classified the remaining 138 images.
    • Both approaches demonstrated high accuracy in automatic OWAS posture registration.

    Conclusions:

    • Marker-less image analysis and computer vision techniques show significant promise for tracking and assessing working postures.
    • Further research is recommended to explore different human models, neural networks, and template matching algorithms.
    • Advancements in these technologies can enhance the utility of the OWAS method for identifying and evaluating potentially harmful work postures.