Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Enrollment
- 41
- Primary Endpoint
- Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Case Control
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Ages
- 18 Years to 65 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •healthy subjects
Exclusion Criteria
- •subjects with musculoskeletal pathologies or other disabling pathologies in progress
Outcomes
Primary Outcomes
Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE
Time Frame: first year
accuracy degree and AucRoc
Secondary Outcomes
No secondary outcomes reported
Investigators
Edda Capodaglio
Principal Investigator
Istituti Clinici Scientifici Maugeri SpA