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Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities
by
Gutiérrez, Manuel
, Gómez, Britam
, Peña, Guisella
, Ortega-Bastidas, Paulina
, Retamal, Gustavo
, Aqueveque, Pablo
, Germany, Enrique
in
Automation
/ Comparative analysis
/ Computer animation
/ Computer programs
/ ergonomics
/ Injuries
/ instrumented OCRA index
/ Motion capture
/ Musculoskeletal diseases
/ musculoskeletal disorders
/ repetitive tasks
/ Rest
/ Risk assessment
/ Sensors
/ Workers
2023
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Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities
by
Gutiérrez, Manuel
, Gómez, Britam
, Peña, Guisella
, Ortega-Bastidas, Paulina
, Retamal, Gustavo
, Aqueveque, Pablo
, Germany, Enrique
in
Automation
/ Comparative analysis
/ Computer animation
/ Computer programs
/ ergonomics
/ Injuries
/ instrumented OCRA index
/ Motion capture
/ Musculoskeletal diseases
/ musculoskeletal disorders
/ repetitive tasks
/ Rest
/ Risk assessment
/ Sensors
/ Workers
2023
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Do you wish to request the book?
Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities
by
Gutiérrez, Manuel
, Gómez, Britam
, Peña, Guisella
, Ortega-Bastidas, Paulina
, Retamal, Gustavo
, Aqueveque, Pablo
, Germany, Enrique
in
Automation
/ Comparative analysis
/ Computer animation
/ Computer programs
/ ergonomics
/ Injuries
/ instrumented OCRA index
/ Motion capture
/ Musculoskeletal diseases
/ musculoskeletal disorders
/ repetitive tasks
/ Rest
/ Risk assessment
/ Sensors
/ Workers
2023
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Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities
Journal Article
Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities
2023
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Overview
In the search to enhance ergonomic risk assessments for upper limb work-related activities, this study introduced and validated the efficiency of an inertial motion capture system, paired with a specialized platform that digitalized the OCRA index. Conducted in a semi-controlled environment, the proposed methodology was compared to traditional risk classification techniques using both inertial and optical motion capture systems. The inertial method encompassed 18 units in a Bluetooth Low Energy tree topology network for activity recording, subsequently analyzed for risk using the platform. Principal outcomes emphasized the optical system’s preeminence, aligning closely with the conventional technique. The optical system’s superiority was further evident in its alignment with the traditional method. Meanwhile, the inertial system followed closely, with an error margin of just ±0.098 compared to the optical system. Risk classification was consistent across all systems. The inertial system demonstrated strong performance metrics, achieving F1-scores of 0.97 and 1 for “risk” and “no risk” classifications, respectively. Its distinct advantage of portability was reinforced by participants’ feedback on its user-friendliness. The results highlight the inertial system’s potential, mirroring the precision of both traditional and optical methods and achieving a 65% reduction in risk assessment time. This advancement mitigates the need for intricate video setups, emphasizing its potential in ergonomic assessments.
Publisher
MDPI AG,MDPI
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