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PRODID:Linklings LLC
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TZID:America/Phoenix
X-LIC-LOCATION:America/Phoenix
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TZOFFSETFROM:-0700
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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203102Z
LOCATION:McArthur Ballroom
DTSTART;TZID=America/Phoenix:20240912T173000
DTEND;TZID=America/Phoenix:20240912T183000
UID:HFESAM_ASPIRE - Presented by HFES_sess108_POS364@linklings.com
SUMMARY:72. Computer Vision Embedded Post-Processing Algorithm on Lifting 
 Risks
DESCRIPTION:Poster\n\nHaozhi Chen, Peiran Liu, and Denny Yu (Purdue Univer
 sity)\n\nThe primary objective of this research is to refine the applicati
 on ergonomic assessment by integrating a computer vision algorithm, which 
 outputs the positions of 17 body joint points. We propose a post-processin
 g algorithm designed to calculate the NIOSH lifting index parameters (HM, 
 VM, DM, AM, CM). The NIOSH lifting index is a widely recognized tool for e
 valuating the potential risk of lifting-related injuries, but current meth
 odologies for obtaining the required data are often manual, subjective, an
 d not conducive to real-time analysis. The pilot project aims to leverage 
 computer vision to capture the dynamics of lifting tasks, automating the e
 valuation process through computer vision. Utilizing coordinates of 17 bod
 y joint points, the algorithm achieved a 90% accuracy rate in classifying 
 the NIOSH lifting index when compared with traditional manual methods. The
 reby enhancing the precision, objectivity, and timeliness of ergonomic eva
 luations.\n\nTrack: Aging, Augmented Cognition, Children's Issues, Communi
 cations, Cybersecurity, Education, Environmental Design, General Sessions,
  Human AI Robot Teaming (AI), Macroergonomics, Occupational Ergonomics, St
 udent Forum, Surface Transportation, Sustainability, System Development
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