Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics

Filetype[PDF-383.74 KB]


  • English
    • Details Supporting Files You May Also Like

    Details:

    • Alternative Title:
      Proc Hum Factors Ergon Soc Annu Meet
    • Personal Author:
    • Description:
      This study explored the use of body posture kinematics derived from wearable inertial sensors to estimate force exertion levels in a two-handed isometric pushing and pulling task. A prediction model was developed grounded on the hypothesis that body postures predictably change depending on the magnitude of the exerted force. Five body postural angles, viz., torso flexion, pelvis flexion, lumbar flexion, hip flexion, and upper arm inclination, collected from 15 male participants performing simulated isometric pushing and pulling tasks in the laboratory were used as predictor variables in a statistical model to estimate handle height (shoulder vs. hip) and force intensity level (low vs. high). Individual anthropometric and strength measurements were also included as predictors. A Random Forest algorithm implemented in a two-stage hierarchy correctly classified 77.2% of the handle height and force intensity levels. Results represent early work in coupling unobtrusive, wearable instrumentation with statistical learning techniques to model occupational activities and exposures to biomechanical risk factors |.
    • Subject:
    • Source:
      Proc Hum Factors Ergon Soc Annu Meet. 61(1):1031-1035
    • Pubmed ID:
      29276370
    • Pubmed Central ID:
      PMC5740231
    • Document Type:
      Journal Article
    • Funding:
      T42 OH008455/OH/NIOSH CDC HHS/United States
    • Collection(s):
    • Main Document Checksum:
    • Download URL:
    • File Type:

    You May Also Like

    Checkout today's featured content at stacks.cdc.gov