Wearable Sensors and Machine Learning for the Assessment of Biomechanical Risk in Lifting Tasks
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 |
|---|---|---|
|
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
Outcome Measures
Primary Outcome Measures
- Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE [first year]
accuracy degree and AucRoc
Eligibility Criteria
Criteria
Inclusion Criteria:
- healthy subjects
Exclusion Criteria:
- subjects with musculoskeletal pathologies or other disabling pathologies in progress
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Istituti Clinici Scientifici Maugeri SpA
Investigators
- Principal Investigator: Edda Capodaglio, PhD, ICS Maugeri IRCCS
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 2475