The talk is titled: The invisible force? Opportunities in Sensors, Computer Vision, and Artificial Intelligence for Determining Worker Exertions

 

Abstract: Overexertion in manual material handling (MMH) tasks is one of the leading causes of occupational injuries. The weight of the load often provides key information for manual injury risk assessments in MMH; however, load weight remains one of the most difficult for ergonomics practitioners to measure in the field as it varies greatly across different objects and is unknown in many circumstances. This talk will provide a brief background on existing techniques before introducing the emerging capabilities of glove sensors, computer vision, and machine learning algorithms for discovering key features that predict worker force exertions. These techniques provide potential tools for ergonomists to overcome the challenge of measuring load weight objectively, an often-critical input for injury risk assessments.