Ignite Creation Date:
2025-12-24 @ 9:12 PM
Ignite Modification Date:
2025-12-25 @ 7:01 PM
Study NCT ID:
NCT05777304
Status:
COMPLETED
Last Update Posted:
2023-12-28
First Post:
2023-03-02
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Wearable Sensors and Machine Learning for the Assessment of Biomechanical Risk in Lifting Tasks
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D000076251', 'term': 'Wearable Electronic Devices'}], 'ancestors': [{'id': 'D055615', 'term': 'Electrical Equipment and Supplies'}, {'id': 'D004864', 'term': 'Equipment and Supplies'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 41}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2010-10-07', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-12', 'completionDateStruct': {'date': '2022-05-06', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-12-21', 'studyFirstSubmitDate': '2023-03-02', 'studyFirstSubmitQcDate': '2023-03-17', 'lastUpdatePostDateStruct': {'date': '2023-12-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-03-21', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-01-24', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE', 'timeFrame': 'first year', 'description': 'accuracy degree and AucRoc'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['inertial measurement units', 'Accelerometers', 'Ergonomics', 'Exposure assessment', 'Lifting', 'Work-related musculoskeletal disorders', 'Machine learning', 'Digital signal processing', 'Risk prediction'], 'conditions': ['Wearable Devices']}, 'referencesModule': {'references': [{'pmid': '36359468', 'type': 'RESULT', '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."}, {'pmid': '36553054', 'type': 'RESULT', '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/diagnostics12123048.'}, {'pmid': '35049162', 'type': 'RESULT', 'citation': "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': '33917206', 'type': 'RESULT', '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(8):2593. doi: 10.3390/s21082593."}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'healthy volunteer referring to treatment clinics for work-related pathologies', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* healthy subjects\n\nExclusion Criteria:\n\n* subjects with musculoskeletal pathologies or other disabling pathologies in progress'}, 'identificationModule': {'nctId': 'NCT05777304', 'briefTitle': 'Wearable Sensors and Machine Learning for the Assessment of Biomechanical Risk in Lifting Tasks', 'organization': {'class': 'OTHER', 'fullName': 'Istituti Clinici Scientifici Maugeri SpA'}, 'officialTitle': 'Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks', 'orgStudyIdInfo': {'id': '2475'}}, 'armsInterventionsModule': {'interventions': [{'name': 'wearable device', 'type': 'DEVICE', 'description': 'IMU sensors and EMG sensors'}]}, 'contactsLocationsModule': {'overallOfficials': [{'name': 'Edda Capodaglio, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'ICS Maugeri IRCCS'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Istituti Clinici Scientifici Maugeri SpA', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Edda Capodaglio', 'investigatorAffiliation': 'Istituti Clinici Scientifici Maugeri SpA'}}}}