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85. Using Wearable Sensors and Deep Learning to Identify Origin and Destination of Lifts
DescriptionWork-related musculoskeletal disorders (WMSDs) represent a significant occupational health concern, especially for workers engaged in lifting activities. Accurately assessing the risk of injury due to lifting tasks is crucial for identifying and mitigating exposures. The Revised NIOSH Lifting Equation (RNLE), has been validated in multiple prospective epidemiologic studies. Traditionally, measuring inputs for RNLE involves intrusive direct measurements that require workers to maintain specific positions while hand locations at the start and end of the lift are measured. However, advancements in wearable technology, such as inertial measurement units (IMUs), have the potential to improve measurement approaches by making them less intrusive, increasing the total number of lifts that can be measured. Therefore, the objective of this study was to validate a model that utilizes IMUs and deep learning methods to identify when a lift occurred as well as the origin and destination of lifts.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Tracks
Aging
Augmented Cognition
Children's Issues
Communications
Cybersecurity
Education
Environmental Design
General Sessions
Human AI Robot Teaming (AI)
Macroergonomics
Occupational Ergonomics
Student Forum
Surface Transportation
Sustainability
System Development