Wearable Devices Clinical Trial
Official title:
Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
NCT number | NCT05777304 |
Other study ID # | 2475 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | October 7, 2010 |
Est. completion date | May 6, 2022 |
Verified date | December 2023 |
Source | Istituti Clinici Scientifici Maugeri SpA |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
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.
Status | Completed |
Enrollment | 41 |
Est. completion date | May 6, 2022 |
Est. primary completion date | January 24, 2022 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years to 65 Years |
Eligibility | Inclusion Criteria: - healthy subjects Exclusion Criteria: - subjects with musculoskeletal pathologies or other disabling pathologies in progress |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
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Istituti Clinici Scientifici Maugeri SpA |
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. — View Citation
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. — View Citation
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 — View Citation
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/diagnostics12123 — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE | accuracy degree and AucRoc | first year |
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