<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04468828</url>
  </required_header>
  <id_info>
    <org_study_id>STUDY 14951</org_study_id>
    <nct_id>NCT04468828</nct_id>
  </id_info>
  <brief_title>Can Micro-Doppler Predict Human Movement?</brief_title>
  <official_title>Validating the Use of Radar Micro-Doppler Characteristics for Accurate Prediction of Human Movement</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Milton S. Hershey Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Milton S. Hershey Medical Center</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The analysis of human motion using radar has become an increasingly active topic of study due&#xD;
      to the diverse applications offered by such an analysis (Lai et al., 2008; Fairchild &amp;&#xD;
      Narayanan, 2016; Narayanan et al., 2014). Information about human motion has important&#xD;
      applications for urban military operations, search-and-rescue missions, surveillance, and&#xD;
      hospital patient monitoring. The micro-motions of human movement in the presence of radar&#xD;
      illumination creates unique modulations in the received signal known as the micro-Doppler&#xD;
      effect. By analyzing these frequency modulations, one can infer the type of movement being&#xD;
      performed. This micro-motion associated with human movement produces a nonlinear and&#xD;
      non-stationary signal that can be characterized using time-frequency domain analysis. Such&#xD;
      signals will be used to identify high injury risk versus low injury risk athletes, which&#xD;
      creates an opportunity to direct limited prevention resources to these high-risk athletes;&#xD;
      identify individuals at risk of falls; and, may even be useful in diagnosing conditions such&#xD;
      as Parkinson's where asymmetrical movement patterns occur as an early indicator.&#xD;
&#xD;
      Traditional methods of movement analysis involve the use of expensive video motion capture&#xD;
      systems that accurately measure the 3-dimensional position of passive reflective markers&#xD;
      affixed to human body landmarks such as joints and body segments, and while motion capture&#xD;
      systems are used to effectively estimate movement dynamics, they are generally not portable,&#xD;
      they are expensive, and they can be cumbersome when the reflective markers are applied to&#xD;
      older persons or persons with movement deficiencies. Drs. Narayanan and Onks have&#xD;
      successfully tested a novel use of Doppler radar that is portable, less expensive, and&#xD;
      eliminates the need for affixing cumbersome reflective markers to participants. In addition,&#xD;
      preliminary testing has demonstrated the ability to discriminate between certain movement&#xD;
      conditions at a level of precision we feel are not obtainable with video motion capture.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Each subject will report for their scheduled data collection in the biomechanics laboratory.&#xD;
      Boney landmarks (shoulder, hip, knee, ankle, etc.) will be used to place small reflective&#xD;
      markers for use with motion capture analysis. The radar will be positioned so that the MDS&#xD;
      data can be captured simultaneously with the motion capture data. Each volunteer will&#xD;
      complete the following activities: • Walk with athletic shoes&#xD;
&#xD;
        -  Walk with bilateral heel lifts in shoes&#xD;
&#xD;
        -  Walk with unilateral heel lift&#xD;
&#xD;
        -  Squat jump with athletic shoes&#xD;
&#xD;
        -  Squat jump with bilateral heel lifts in shoes&#xD;
&#xD;
        -  Squat Jump with unilateral heel lift&#xD;
&#xD;
        -  Stationary standing posture with athletic shoes&#xD;
&#xD;
        -  Stationary standing posture with bilateral heel lifts in shoes&#xD;
&#xD;
        -  Stationary standing posture with unilateral heel lift Each activity will be performed&#xD;
           three times toward the radar in order to assess repeatability and reliability, and also&#xD;
           for maintaining adequate statistical training and testing datasets for confirming the&#xD;
           use of previous established classification algorithms. These algorithms will again be&#xD;
           used to calculate prediction accuracy for the different activities (walking, jumping,&#xD;
           and stationary posture) and different footwear conditions (shoes without inserts, shoes&#xD;
           with bilateral inserts, shoes with unilateral inserts). The biomechanical motion capture&#xD;
           data will be processed similarly to the MDS data to compare to accuracy between the two&#xD;
           methods for the same observed task-specific differences. Our goal is to determine if MDS&#xD;
           can achieve the same measurement accuracy as motion capture for the same task. Since&#xD;
           biomechanical motion capture is the &quot;gold standard&quot; of human movement measurement, the&#xD;
           successful completion of this aim will establish the validity of MDS as an effective&#xD;
           clinical measure of human movement.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Withdrawn</overall_status>
  <why_stopped>
    Lack of funding&#xD;
  </why_stopped>
  <start_date type="Anticipated">July 2021</start_date>
  <completion_date type="Anticipated">January 2022</completion_date>
  <primary_completion_date type="Anticipated">January 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>The primary endpoint of this proposed study will compare MDS to biomechanical motion capture in terms of accuracy of discriminating footwear.</measure>
    <time_frame>40 minutes</time_frame>
    <description>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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>To classify different human movement features by pairing different types of human movement to their component features.</measure>
    <time_frame>40 minutes</time_frame>
    <description>This analysis will help to establish a radar MDS &quot;library&quot; of features matched with specific types of movements.</description>
  </secondary_outcome>
  <enrollment type="Actual">0</enrollment>
  <condition>Pain Management</condition>
  <condition>Injury Prevention</condition>
  <eligibility>
    <study_pop>
      <textblock>
        Subjects will be recruited from among athletes attending a Division 3 college.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Adults aged 18-25&#xD;
&#xD;
          -  Able to effectively read, write and understand English&#xD;
&#xD;
          -  Must be able to walk, jog, and jump without a limp&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Children 0-17 years of age&#xD;
&#xD;
          -  Unable to effectively read, write and understand English&#xD;
&#xD;
          -  Subjects with conditions that limit the ability to walk, jog, or jump&#xD;
&#xD;
          -  History of hip or knee surgery&#xD;
&#xD;
          -  History of cerebral vascular accident&#xD;
&#xD;
          -  Subjects who are pregnant&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>25 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Cayce Onks, DO</last_name>
    <role>Principal Investigator</role>
    <affiliation>Milton S. Hershey Medical Center</affiliation>
  </overall_official>
  <verification_date>November 2021</verification_date>
  <study_first_submitted>July 9, 2020</study_first_submitted>
  <study_first_submitted_qc>July 9, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 13, 2020</study_first_posted>
  <last_update_submitted>November 3, 2021</last_update_submitted>
  <last_update_submitted_qc>November 3, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">November 10, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Milton S. Hershey Medical Center</investigator_affiliation>
    <investigator_full_name>Cayce Onks</investigator_full_name>
    <investigator_title>Associate Professor</investigator_title>
  </responsible_party>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04468828
org study id: STUDY 14951
nct id: NCT04468828
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Cohort
time perspective: Prospective
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
description: 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.
measure: To classify different human movement features by pairing different types of human movement to their component features.
time frame: 40 minutes
description: This analysis will help to establish a radar MDS "library" of features matched with specific types of movements.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 25 Years
healthy volunteers: Accepts Healthy Volunteers
last name: Cayce Onks, DO
role: Principal Investigator
affiliation: Milton S. Hershey Medical Center
responsible party type: Principal Investigator
investigator affiliation: Milton S. Hershey Medical Center
investigator full name: Cayce Onks
investigator title: Associate Professor
sharing ipd: No