Wearable DevicesNCT05777304
Wearable Sensors and Machine Learning for the Assessment of Biomechanical Risk in Lifting Tasks
An observational study of Wearable Devices, run by Istituti Clinici Scientifici Maugeri SpA.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Wearable Devices
- What is being tested?
- wearable device
- Type of treatment
- Medical device
- How many people
- 41 participants
- Where
- No sites listed yet
- Study dates
- 7 Oct 2010to 6 May 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: healthy volunteer referring to treatment clinics for work-related pathologies
Who may be able to join
- healthy subjects
Who cannot join
- subjects with musculoskeletal pathologies or other disabling pathologies in progress
About this study
Official title: Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
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.
Read the 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.
- Conditions
- Wearable Devices
- Being tested
- wearable device
- Keywords
- inertial measurement units, Accelerometers, Ergonomics, Exposure assessment, Lifting, Work-related musculoskeletal disorders, Machine learning, Digital signal processing, Risk prediction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
wearable deviceDevice
IMU sensors and EMG sensors
What is being measured
The primary outcome is the main question the study is designed to answer.
Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE
accuracy degree and AucRoc
Measured: first year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Mar 2023
- Study start7 Oct 2010
- Primary completion24 Jan 2022
- Study completion6 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Wearable DevicesStudies of the same condition, open and under way.
- More trials run by Istituti Clinici Scientifici Maugeri SpAThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 41 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpA
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Edda Capodaglio, Istituti Clinici Scientifici Maugeri SpA
- Investigators
- Edda Capodaglio, PhD, Principal investigator, ICS Maugeri IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05777304
- Sponsor's protocol ID
- 2475
- Status last verified
- Dec 2023
- First submitted
- 2 Mar 2023
- Last updated
- 28 Dec 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 36359468Donisi 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.
PubMed 36553054Donisi 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.
PubMed 35049162Donisi 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.
PubMed 33917206
Study at a glance
From the registry record, last updated by the sponsor on 28 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
- ClinicalTrials.gov ID
- NCT05777304
- Acronym / sponsor ID
- 2475
- Conditions
- Wearable Devices
- Keywords
- inertial measurement units, Accelerometers, Ergonomics, Exposure assessment, Lifting, Work-related musculoskeletal disorders, Machine learning, Digital signal processing
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 41 participants (actual)
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpAUniversity, hospital or other
- Study start
- 7 Oct 2010 (actual)
- Primary completion
- 24 Jan 2022 (actual)
- Study completion
- 6 May 2022 (actual)
- First posted
- 21 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Observational model
- Case-control
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- healthy volunteer referring to treatment clinics for work-related pathologies
Key inclusion criteria (1 listed)
- healthy subjects
Key exclusion criteria (1 listed)
- subjects with musculoskeletal pathologies or other disabling pathologies in progress
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| wearable deviceIMU sensors and EMG sensors | Device | – |
Primary outcome
Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE
Time frame: first year
accuracy degree and AucRoc
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Dec 2023.
- Results publication: PubMed 36359468Donisi 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.
- Results publication: PubMed 36553054Donisi 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.
- Results publication: PubMed 35049162Donisi 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.
- Results publication: PubMed 33917206Donisi 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Edda Capodaglio, PhD principal investigator
ICS Maugeri IRCCS
Questions about this trial
What is this clinical trial studying?
An observational study of Wearable Devices, run by Istituti Clinici Scientifici Maugeri SpA.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Validation of the proposed strategy to assess the risk of lifting activities, according to RNLE (time frame: first year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05777304, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Dec 2023.