Inertial Sensors for Ergonomics Applications

Project Leads: Clive D'Souza, Sol Lim

Student Researchers: Andrea Case, Angjela Keci, Chanmee Chung, Yuanxin Zheng


PROJECT OBJECTIVES

Excessive physical workload resulting from prolonged manual work in awkward constrained postures and high force exertions are known risk factors for musculoskeletal injuries. Traditional ergonomics methods such as video recording, 3D motion capture and electrogoniometry for recording body postures at work sites are cumbersome, obtrusive and challenging. This project aims to develop new methods for ergonomics posture assessment using low-cost body-worn inertial sensors.

SPECIFIC AIMS
  • Develop kinematics based algorithms using inertial sensor data from a controlled lab study to classify body postures and generate profiles of exposure to postural demand and occupational injury risk in manual work
  • Validate these algorithms in a field study of hospital lift-team staff performing patient handling tasks at a local hospital.

G-Sensor
Image showing a sample inertial sensing unit, G-Sensor (BTS Engineering) used in our lab.



LowRes Picture
Image of a shoulder-height push task.

LowRes Picture
Participant lifting a box using the squat posture.

LowRes Picture
Stock image illustrating manual labor required by nurses, resulting in high numbers of low-back injuries.
Stock image provided by www.blog.arjohuntleigh.com/
HOW IS INERTIAL SENSING USEFUL IN ERGONOMICS?

This project will validate use of low-cost body-mounted inertial sensors for ergonomics posture assessment. The project will result in a preliminary methodology for using IMUs in more sensitive and continuous occupational exposure assessment and sets the stage for future studies examining occupational exposures among healthcare workers in diverse patient-care settings. The ability to classify body postures and generate more precise exposure profiles would significantly improve our capability to assess injury risk, identify prevention strategies and evaluate effectiveness of interventions over prolonged periods of time.

In the context of patient handling, this project will generate metrics to objectively evaluate posture demand and workload in lift team staff and to refine theories on the mechanism and progression of injury risk in healthcare workers.

PROJECT SPONSORS

This work is funded by a Pilot Project Research Traineeship award to S. Lim through the U-M Center for Occupational Health and Safety Engineering Training Grant #T42-OH008455 from the National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention.


PROJECT OUTCOMES AND PUBLICATIONS