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Wearable Sensors and Machine Learning for the Assessment of Biomechanical Risk in Lifting Tasks

NCT05777304 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 41

Last updated 2023-12-28

No results posted yet for this study

Summary

Lifting loads can cause work-related musculoskeletal disorders. The National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency, and other geometrical characteristics of lifting. Body-worn inertial sensor technology provides a number of opportunities to advance the safety and health of workers engaged in physical work. Motion-tracking systems together with Machine learning (ML) algorithms are used in the ergonomic field for biomechanical risk assessment by means of data acquired by wearable inertial systems. The investigators posed the question whether it is possible to classify lifting tasks belonging to different risk classes according to the value of LI using a machine learning approach by means of features extracted from raw signals. Aim of this study was to develop and validate, through ML algorithms, a non-invasive detection system of kinetic-kinematic parameters using IMU and EMG sensors, for the ergonomic assessment of the risk associated with a load lifting activity.

Conditions

  • Wearable Devices

Interventions

DEVICE

wearable device

IMU sensors and EMG sensors

Sponsors & Collaborators

  • Istituti Clinici Scientifici Maugeri SpA

    lead OTHER

Principal Investigators

  • Edda Capodaglio, PhD · ICS Maugeri IRCCS

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2010-10-07
Primary Completion
2022-01-24
Completion
2022-05-06

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05777304 on ClinicalTrials.gov