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

NCT05777304 | Completed

• 1 other identifier: 2475
• Study Type: observational
• Target: 41
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Wearable Devices

Keywords

inertial measurement units; Accelerometers; Ergonomics; Exposure assessment; Lifting; Work-related musculoskeletal disorders; Machine learning; Digital signal processing; Risk prediction

Outcome Measures

Primary Outcomes (1)

• Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE

  accuracy degree and AucRoc

  first year

Interventions

wearable device DEVICE

IMU sensors and EMG sensors

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

healthy volunteer referring to treatment clinics for work-related pathologies

You may qualify if:

• healthy subjects

You may not qualify if:

• subjects with musculoskeletal pathologies or other disabling pathologies in progress

Sponsors & Collaborators

• Istituti Clinici Scientifici Maugeri SpA: lead

Related Publications (4)

• Donisi L, Cesarelli G, Capodaglio E, Panigazzi M, D'Addio G, Cesarelli M, Amato F. A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks. Diagnostics (Basel). 2022 Oct 29;12(11):2624. doi: 10.3390/diagnostics12112624.

  PMID: 36359468 RESULT

• Donisi L, Cesarelli G, Pisani N, Ponsiglione AM, Ricciardi C, Capodaglio E. Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics (Basel). 2022 Dec 5;12(12):3048. doi: 10.3390/diagnostics12123048.

  PMID: 36553054 RESULT

• Donisi L, Capodaglio EM, Amitrano F, Cesarelli G, Pagano G, D'Addio G. A multiple linear regression approach to extimate lifted load from features extracted from inertial data. G Ital Med Lav Ergon. 2021 Dec;43(4):373-378.

  PMID: 35049162 RESULT

• Donisi L, Cesarelli G, Coccia A, Panigazzi M, Capodaglio EM, D'Addio G. Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning. Sensors (Basel). 2021 Apr 7;21(8):2593. doi: 10.3390/s21082593.

  PMID: 33917206 RESULT

MeSH Terms

Interventions

Wearable Electronic Devices

Intervention Hierarchy (Ancestors)

Electrical Equipment and Supplies; Equipment and Supplies

Study Officials

• Edda Capodaglio, PhD

  ICS Maugeri IRCCS

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 2, 2023
• First Posted: March 21, 2023
• Study Start: October 7, 2010
• Primary Completion: January 24, 2022
• Study Completion: May 6, 2022
• Last Updated: December 28, 2023

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share