Auto-AzKNIOSH: an automatic NIOSH evaluation with Azure Kinect coupled with task recognition.
Standard Ergonomic Risk Assessment (ERA) from video analysis is a highly time-consuming activity and is affected by the subjectivity of ergonomists. Motion Capture (MOCAP) addresses these limitations by allowing objective ERA. Here a depth camera, one of the most commonly used MOCAP systems for ERA...
| Publicado en: | Ergonomics Vol. 68; no. 10; pp. 1718 - 1735 |
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| Autores principales: | , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188122501&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188122501 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Oct2025 vid: 68 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 188122501 181428765 188122501 188122501 10.1080/00140139.2024.2433027 188122501 ppf: 1718 ppct: 17 formats: tig: atl: Auto-AzKNIOSH: an automatic NIOSH evaluation with Azure Kinect coupled with task recognition. aug: au: Lolli, Francesco Coruzzolo, Antonio Maria Forgione, Chiara Peron, Mirco Sgarbossa, Fabio affil: Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy sug: subj: Ergonomics Evaluation Lifting Physiology Occupational Safety Evaluation Convolutional Neural Networks Evaluation Occupational-Related Injuries Risk Factors Risk Assessment Task Performance and Analysis Evaluation Videorecording National Institute for Occupational Safety and Health Human Funding Source Motion Capture Biomechanics Movement Evaluation Balance, Postural Motion Analysis Systems Software Design Comparative Studies Kinematics T-Tests Benchmarking ab: Standard Ergonomic Risk Assessment (ERA) from video analysis is a highly time-consuming activity and is affected by the subjectivity of ergonomists. Motion Capture (MOCAP) addresses these limitations by allowing objective ERA. Here a depth camera, one of the most commonly used MOCAP systems for ERA (i.e. Azure Kinect), is used for the evaluation of the NIOSH Lifting Equation exploiting a tool named AzKNIOSH. First, to validate the tool, we compared its performance with those provided by a commercial software, Siemens Jack TAT, based on an Inertial Measurement Units (IMUs) suit and found a high agreement between them. Secondly, a Convolutional Neural Network (CNN) was employed for task recognition, automatically identifying the lifting actions. This procedure was evaluated by comparing the results obtained from manual detection with those obtained through automatic detection. Thus, through automated task detection and the implementation of Auto-AzKNIOSH we achieved a fully automated ERA. Practitioner Summary: The standard evaluation of the NIOSH Lifting Equation is time-consuming and subjective, thus a new automatic tool is designed, which integrates motion captures provided by Azure Kinect and task recognition. We found a high agreement between our tool and Siemens Jack TAT suit, the golden standard technology for motion capture. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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