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

Istituti Clinici Scientifici Maugeri SpA (Other)
Overall Status
CT.gov ID

Study Details

Study Description

Brief 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.

Condition or Disease Intervention/Treatment Phase
  • Device: wearable device

Detailed Description

The study envisages the voluntary enrollment of healthy subjects, referring to treatment clinics for work-related pathologies (excluding subjects aged <18 or > 65 years, and those with musculoskeletal pathologies or other disabling pathologies in progress), to carry out two repeated lifting tests. The two tests are set up to correspond respectively to the two NIOSH risk classes (LI<1, NO RISK; and LI>1, RISK). The IMU sensors provide wirelessly a series of data from which it is intended to extract a number of features (feature extraction) that have a high predictive power, through the digital signal processing technique using dedicated software (i.e. Matlab, SPSS). In a second step, data obtained from EMG sensors will be added to the analysis. Among the different artificial intelligence algorithms, the investigator will look for those most able to discriminate the various risk classes on the basis of the parameters extracted from the signals detected during the motor task.

Study Design

Study Type:
Actual Enrollment :
41 participants
Observational Model:
Time Perspective:
Official Title:
Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
Actual Study Start Date :
Oct 7, 2010
Actual Primary Completion Date :
Jan 24, 2022
Actual Study Completion Date :
May 6, 2022

Outcome Measures

Primary Outcome Measures

  1. Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE [first year]

    accuracy degree and AucRoc

Eligibility Criteria


Ages Eligible for Study:
18 Years to 65 Years
Sexes Eligible for Study:
Accepts Healthy Volunteers:
Inclusion Criteria:
  • healthy subjects
Exclusion Criteria:
  • subjects with musculoskeletal pathologies or other disabling pathologies in progress

Contacts and Locations


No locations specified.

Sponsors and Collaborators

  • Istituti Clinici Scientifici Maugeri SpA


  • Principal Investigator: Edda Capodaglio, PhD, ICS Maugeri IRCCS

Study Documents (Full-Text)

None provided.

More Information


Responsible Party:
Edda Capodaglio, Principal Investigator, Istituti Clinici Scientifici Maugeri SpA
ClinicalTrials.gov Identifier:
Other Study ID Numbers:
  • 2475
First Posted:
Mar 21, 2023
Last Update Posted:
Mar 21, 2023
Last Verified:
Mar 1, 2023
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD:
Studies a U.S. FDA-regulated Drug Product:
Studies a U.S. FDA-regulated Device Product:
Keywords provided by Edda Capodaglio, Principal Investigator, Istituti Clinici Scientifici Maugeri SpA

Study Results

No Results Posted as of Mar 21, 2023