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

NCT ID: NCT05777304

Last Updated: 2023-12-28

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 41 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2010-10-07
• Study Completion Date: 2022-05-06

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.

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.

Conditions

Wearable Devices

Keywords

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

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Interventions

wearable device

IMU sensors and EMG sensors

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• healthy subjects

Exclusion Criteria

• subjects with musculoskeletal pathologies or other disabling pathologies in progress

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Istituti Clinici Scientifici Maugeri SpA

OTHER

Sponsor Role: lead

Responsible Party

Edda Capodaglio

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Edda Capodaglio, PhD

Role: PRINCIPAL_INVESTIGATOR

ICS Maugeri IRCCS

References

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.

Reference Type: RESULT

PMID: 36359468 (View on PubMed)

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.

Reference Type: RESULT

PMID: 36553054 (View on PubMed)

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.

Reference Type: RESULT

PMID: 35049162 (View on PubMed)

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.

Reference Type: RESULT

PMID: 33917206 (View on PubMed)

Other Identifiers

2475

Identifier Type: -

Identifier Source: org_study_id