Pain ManagementNCT04468828
Can Micro-Doppler Predict Human Movement?
An observational study of Pain Management and Injury Prevention, run by Milton S. Hershey Medical Center.
Trial summary
The analysis of human motion using radar has become an increasingly active topic of study due to the diverse applications offered by such an analysis (Lai et al., 2008; Fairchild \& Narayanan, 2016; Narayanan et al., 2014). Read more about this study
- Who is it for?
- Ages 18 to 25 yearsHealthy volunteers can take part
- Conditions the study is for
- Pain Management, Injury Prevention
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- Jul 2021to Jan 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 25 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Subjects will be recruited from among athletes attending a Division 3 college.
Who may be able to join
- Adults aged 18-25
- Able to effectively read, write and understand English
- Must be able to walk, jog, and jump without a limp
Who cannot join
- Children 0-17 years of age
- Unable to effectively read, write and understand English
- Subjects with conditions that limit the ability to walk, jog, or jump
- History of hip or knee surgery
- History of cerebral vascular accident
- Subjects who are pregnant
About this study
Official title: Validating the Use of Radar Micro-Doppler Characteristics for Accurate Prediction of Human Movement
The analysis of human motion using radar has become an increasingly active topic of study due to the diverse applications offered by such an analysis (Lai et al., 2008; Fairchild \& Narayanan, 2016; Narayanan et al., 2014). Information about human motion has important applications for urban military operations, search-and-rescue missions, surveillance, and hospital patient monitoring. The micro-motions of human movement in the presence of radar illumination creates unique modulations in the received signal known as the micro-Doppler effect. By analyzing these frequency modulations, one can infer the type of movement being performed. This micro-motion associated with human movement produces a nonlinear and non-stationary signal that can be characterized using time-frequency domain analysis. Such signals will be used to identify high injury risk versus low injury risk athletes, which creates an opportunity to direct limited prevention resources to these high-risk athletes; identify individuals at risk of falls; and, may even be useful in diagnosing conditions such as Parkinson's where asymmetrical movement patterns occur as an early indicator.
Traditional methods of movement analysis involve the use of expensive video motion capture systems that accurately measure the 3-dimensional position of passive reflective markers affixed to human body landmarks such as joints and body segments, and while motion capture systems are used to effectively estimate movement dynamics, they are generally not portable, they are expensive, and they can be cumbersome when the reflective markers are applied to older persons or persons with movement deficiencies. Drs. Narayanan and Onks have successfully tested a novel use of Doppler radar that is portable, less expensive, and eliminates the need for affixing cumbersome reflective markers to participants. In addition, preliminary testing has demonstrated the ability to discriminate between certain movement conditions at a level of precision we feel are not obtainable with video motion capture.
Read the detailed description
Each subject will report for their scheduled data collection in the biomechanics laboratory. Boney landmarks (shoulder, hip, knee, ankle, etc.) will be used to place small reflective markers for use with motion capture analysis. The radar will be positioned so that the MDS data can be captured simultaneously with the motion capture data. Each volunteer will complete the following activities: • Walk with athletic shoes
- Walk with bilateral heel lifts in shoes
- Walk with unilateral heel lift
- Squat jump with athletic shoes
- Squat jump with bilateral heel lifts in shoes
- Squat Jump with unilateral heel lift
- Stationary standing posture with athletic shoes
- Stationary standing posture with bilateral heel lifts in shoes
- Stationary standing posture with unilateral heel lift Each activity will be performed three times toward the radar in order to assess repeatability and reliability, and also for maintaining adequate statistical training and testing datasets for confirming the use of previous established classification algorithms. These algorithms will again be used to calculate prediction accuracy for the different activities (walking, jumping, and stationary posture) and different footwear conditions (shoes without inserts, shoes with bilateral inserts, shoes with unilateral inserts). The biomechanical motion capture data will be processed similarly to the MDS data to compare to accuracy between the two methods for the same observed task-specific differences. Our goal is to determine if MDS can achieve the same measurement accuracy as motion capture for the same task. Since biomechanical motion capture is the "gold standard" of human movement measurement, the successful completion of this aim will establish the validity of MDS as an effective clinical measure of human movement.
- Conditions
- Pain Management, Injury Prevention
- Medical subject headings
- Agnosia
What is being measured
The primary outcome is the main question the study is designed to answer.
The primary endpoint of this proposed study will compare MDS to biomechanical motion capture in terms of accuracy of discriminating footwear.
We expect that when the motion capture data is processed similarly to the MDS data, the predictive accuracy of the MDS approach will be at least as good as that of the motion capture approach.
Measured: 40 minutes
Secondary outcomes (1)
To classify different human movement features by pairing different types of human movement to their component features.
This analysis will help to establish a radar MDS "library" of features matched with specific types of movements.
Measured: 40 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jul 2020
- Study startJul 2021 (estimated)
- Primary completionJan 2022 (estimated)
- Study completionJan 2022 (estimated)
Why it stopped: Lack of funding
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pain ManagementStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Milton S. Hershey Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Cayce Onks, Milton S. Hershey Medical Center
- Investigators
- Cayce Onks, DO, Principal investigator, Milton S. Hershey Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04468828
- Sponsor's protocol ID
- STUDY 14951
- Status last verified
- Nov 2021
- First submitted
- 9 Jul 2020
- Last updated
- 10 Nov 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validating the Use of Radar Micro-Doppler Characteristics for Accurate Prediction of Human Movement
- ClinicalTrials.gov ID
- NCT04468828
- Acronym / sponsor ID
- STUDY 14951
- Conditions
- Pain Management, Injury Prevention
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Lack of funding
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Milton S. Hershey Medical CenterUniversity, hospital or other
- Study start
- Jul 2021 (estimated)
- Primary completion
- Jan 2022 (estimated)
- Study completion
- Jan 2022 (estimated)
- First posted
- 13 Jul 2020 (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.
The analysis of human motion using radar has become an increasingly active topic of study due to the diverse applications offered by such an analysis (Lai et al., 2008; Fairchild \& Narayanan, 2016; Narayanan et al., 2014). Information about human motion has important applications for urban military operations, search-and-rescue missions, surveillance, and hospital patient monitoring. The micro-motions of human movement in the presence of radar illumination creates unique modulations in the received signal known as the micro-Doppler effect. By analyzing these frequency modulations, one can infer the type of movement being performed. This micro-motion associated with human movement produces a nonlinear and non-stationary signal that can be characterized using time-frequency domain analysis. Such signals will be used to identify high injury risk versus low injury risk athletes, which creates an opportunity to direct limited prevention resources to these high-risk athletes; identify individuals at risk of falls; and, may even be useful in diagnosing conditions such as Parkinson's where asymmetrical movement patterns occur as an early indicator.
Traditional methods of movement analysis involve the use of expensive video motion capture systems that accurately measure the 3-dimensional position of passive reflective markers affixed to human body landmarks such as joints and body segments, and while motion capture systems are used to effectively estimate movement dynamics, they are generally not portable, they are expensive, and they can be cumbersome when the reflective markers are applied to older persons or persons with movement deficiencies. Drs. Narayanan and Onks have successfully tested a novel use of Doppler radar that is portable, less expensive, and eliminates the need for affixing cumbersome reflective markers to participants. In addition, preliminary testing has demonstrated the ability to discriminate between certain movement conditions at a level of precision we feel are not obtainable with video motion capture.
Detailed description
Each subject will report for their scheduled data collection in the biomechanics laboratory. Boney landmarks (shoulder, hip, knee, ankle, etc.) will be used to place small reflective markers for use with motion capture analysis. The radar will be positioned so that the MDS data can be captured simultaneously with the motion capture data. Each volunteer will complete the following activities: • Walk with athletic shoes
- Walk with bilateral heel lifts in shoes
- Walk with unilateral heel lift
- Squat jump with athletic shoes
- Squat jump with bilateral heel lifts in shoes
- Squat Jump with unilateral heel lift
- Stationary standing posture with athletic shoes
- Stationary standing posture with bilateral heel lifts in shoes
- Stationary standing posture with unilateral heel lift Each activity will be performed three times toward the radar in order to assess repeatability and reliability, and also for maintaining adequate statistical training and testing datasets for confirming the use of previous established classification algorithms. These algorithms will again be used to calculate prediction accuracy for the different activities (walking, jumping, and stationary posture) and different footwear conditions (shoes without inserts, shoes with bilateral inserts, shoes with unilateral inserts). The biomechanical motion capture data will be processed similarly to the MDS data to compare to accuracy between the two methods for the same observed task-specific differences. Our goal is to determine if MDS can achieve the same measurement accuracy as motion capture for the same task. Since biomechanical motion capture is the "gold standard" of human movement measurement, the successful completion of this aim will establish the validity of MDS as an effective clinical measure of human movement.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 25 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Subjects will be recruited from among athletes attending a Division 3 college.
Key inclusion criteria (3 listed)
- Adults aged 18-25
- Able to effectively read, write and understand English
- Must be able to walk, jog, and jump without a limp
Key exclusion criteria (6 listed)
- Children 0-17 years of age
- Unable to effectively read, write and understand English
- Subjects with conditions that limit the ability to walk, jog, or jump
- History of hip or knee surgery
- History of cerebral vascular accident
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The primary endpoint of this proposed study will compare MDS to biomechanical motion capture in terms of accuracy of discriminating footwear.
Time frame: 40 minutes
We expect that when the motion capture data is processed similarly to the MDS data, the predictive accuracy of the MDS approach will be at least as good as that of the motion capture approach.
1 secondary outcome
To classify different human movement features by pairing different types of human movement to their component features.
Time frame: 40 minutes
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Nov 2021.
- 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.
Cayce Onks, DO principal investigator
Milton S. Hershey Medical Center
Questions about this trial
What is this clinical trial studying?
An observational study of Pain Management and Injury Prevention, run by Milton S. Hershey Medical Center.
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 3 inclusion and 6 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: withdrawn, as of the sponsor's last update on 10 Nov 2021. 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 was withdrawn before enrolling participants, so there are no results.
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?
The primary endpoint of this proposed study will compare MDS to biomechanical motion capture in terms of accuracy of discriminating footwear. (time frame: 40 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 10 Nov 2021.