PSHFES Webinar - Smart Ergo Posture Assessment Tool

Mobile, automated, posture assessment tools are becoming increasingly capable of basic posture data collection and evaluation of simple work tasks.

Please join us for a webinar presentation by Behnoosh Parsa, recent University of Washington Ph.D. graduate. (now working at Amazon Robotics Strategy)

Behnoosh will describe the development of her smart ergo posture assessment tool, and demonstrate its use.

• How the tool was developed, its current capabilities, and potential future enhancements.
• Examples of the tool’s use.

We encourage you to download the tool and come with questions.  The app can be dowloaded via the Apple Store or on Google Play:

Apple Store:

https://apps.apple.com/bw/app/ergonomics-risk-assessment-app/id1497234474#?platform=iphone

Google Play:

https://play.google.com/store/apps/details?id=com.ergoapp2

Speaker Bio:

Behnoosh Parsa

Behnoosh Parsa is an Applied Scientist at Amazon Robotics Strategy. Behnoosh graduated from the University of Washington Mechanical Engineering Ph.D. program 2020 with a Technology Entrepreneurship Certificate from Foster School of Business in autumn. She received a bachelor’s degree in Biomedical Engineering with a minor in Industrial Engineering from Tehran Polytechnic (Amirkabir University) in 2013. She moved to the US for graduate school and earned a master’s in human motor control (Kinesiology) at Penn State with minors in Mechanical Engineering and Computational Science in 2016.

About the Ergonomics Risk Assessment App

LinkedIn:

https://www.linkedin.com/feed/update/urn:li:activity:6696244126347870208/

This mobile application is built to help identify ergonomically unsafe actions. It uses artificial intelligence technology to detect human pose and uses Rapid Entire Body Assessment (REBA) to approximate the risk of developing an injury.

REBA is a common method used in industry and it computes an index between 1 (lowest risk level) to 15 (highest risk level). REBA uses the joint angles we get from the detected pose and compares them with the corresponding safe range of motion.

This app detects a single person on a screen and evaluates REBA for that person. Based on REBA it classifies the activities into “low risk”, “medium risk”, and “high risk” and it shows the skeleton green, yellow and red, correspondingly.

Thanks to the Department of Labor & Industries for their support of this project.