Wearable Sensors and Machine Learning for the Assessment... | NCT05777304

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

NCT05777304

Sponsor

Istituti Clinici Scientifici Maugeri SpA

Status

Completed

Last Update Posted

Dec 28, 2023Actual

Enrollment

41Actual

Phase

Not provided

Conditions

Wearable Devices

Interventions

wearable device

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Dec 2023

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Oct 7, 2010Actual

Primary Completion Date

Jan 24, 2022Actual

Completion Date

May 6, 2022Actual

First Submitted Date

Mar 2, 2023

First Submission Date that Met QC Criteria

Mar 17, 2023

First Posted Date

Mar 21, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Dec 21, 2023

Last Update Posted Date

Dec 28, 2023Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Edda Capodaglio, Principal Investigator, Istituti Clinici Scientifici Maugeri SpAPrincipal Investigator

Lead Sponsor

Istituti Clinici Scientifici Maugeri SpAOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

41Actual

Name	Type	Description	Arm Group Labels	Other Names
wearable device	Device	IMU sensors and EMG sensors

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE	accuracy degree and AucRoc	first year

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

healthy subjects

Exclusion Criteria:

subjects with musculoskeletal pathologies or other disabling pathologies in progress

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

65 Years

Standard Ages

AdultOlder Adult

Study Population

healthy volunteer referring to treatment clinics for work-related pathologies

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Edda Capodaglio, PhD	ICS Maugeri IRCCS	Principal Investigator

Locations

Not provided

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
36359468	Result	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.
36553054	Result	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.

ID	Term
D055615	Electrical Equipment and Supplies
D004864	Equipment and Supplies