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Related Experiment Video

Updated: Feb 19, 2026

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Dynamic and static shoulder strength relationship and predictive model.

Alison C McDonald¹, Spencer M Savoie¹, Daanish M Mulla¹

¹Occupational Biomechanics Laboratory, Department of Kinesiology, McMaster University, Hamilton, ON, L8S 4K1, Canada.

Applied Ergonomics

|November 11, 2017

Summary

This summary is machine-generated.

This study developed predictive equations for dynamic shoulder strength using static measurements. These models can help establish safe force limits for workplace tasks, preventing over-exertion injuries.

Keywords:

Shoulder Strength Workplace design

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Last Updated: Feb 19, 2026

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Published on: March 12, 2021

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Published on: February 10, 2015

Area of Science:

Biomechanics
Occupational Health
Ergonomics

Background:

Workplace tasks are often dynamic, but static strength is used to prevent injuries.
Assessing dynamic shoulder strength is crucial for understanding occupational exertion.

Purpose of the Study:

To identify factors influencing isokinetic shoulder strength.
To create predictive equations for shoulder flexion and extension strength using isometric measurements.

Main Methods:

Fifteen women performed isokinetic and isometric shoulder exertions in various planes, velocities, and grips.
Stepwise multiple regression models were developed to predict strength.

Main Results:

Predictive models explained significant variance in isokinetic shoulder flexion (R² = 0.59) and extension (R² = 0.67).
Models utilized a subset of four and five input variables, respectively.

Conclusions:

Developed equations can predict dynamic shoulder strength from static measures.
These predictions aid in setting safe force limits for dynamic occupational tasks.