Micro-Doppler Radar: A Gold Standard Comparison
- Conditions
- Musculoskeletal InjuryAnterior Cruciate Ligament Injuries
- Registration Number
- NCT05521126
- Lead Sponsor
- Milton S. Hershey Medical Center
- Brief Summary
The purpose of this study is to see if the study team can use micro-Doppler signal (MDS) technology to determine if someone has had an anterior cruciate ligament (ACL) reconstruction. The investigators will do this by comparing the movement data from a group of people who have had the surgery with a group who has not had the surgery to see if the micro-Doppler radar technology can accurately and predictably tell the difference.
- Detailed Description
The objective of this research is to validate that radar MDS can accurately and predictably differentiate individuals at high-risk for MSKI from those who are low risk. The investigators hypothesize that MDS will identify individuals at a high-risk for MSKI more accurately than the gold-standard MC technologies. To test this hypothesis, the investigators propose a case control study that will compare adults who have undergone ACL reconstruction to a control group of healthy adults that has not. Patients who have undergone ACL reconstruction have a 6-24% chance of either re-tearing their ACL or having a subsequent knee surgery on either side within two years of successful completion of surgery and post-surgical rehabilitation. Despite being released for full activities, little is known about what makes this group at high-risk for re-tear. As such, the investigators will use this patient population as a model for identifying an at-risk population for musculoskeletal injury (MSKI). The researchers will simultaneously collect radar micro-Doppler signals and biomechanical motion capture (MC) data in a state-of-the-art human movement lab. Participants will be asked to perform a series of functional activities that will be captured by both the MDS radar and MC systems. The data sets will then be analyzed independently.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 250
High risk cohort
- age 18-40
- history of ACL reconstruction
- no current musculoskeletal injuries
- ACL repair between 9 and 24 months prior to recruitment
Control cohort
- age 18-40
- never had lower extremity surgery
High risk cohort
- age <18 or >40
- pregnancy
- institutionalization
- history of cerebral vascular accident
- unable to provide informed consent
- inability to perform study activities
- history of hip or knee replacement
- inability to walk or jump without a limp
- current neuromuscular disease
- any surgery in the last 6 months
Control cohort
- age < 18 or > 40
- pregnancy
- institutionalization
- history of Cerebral Vascular Accident
- unable to provide informed consent
- inability to perform study activities
- history of knee or hip replacement
- inability to walk or jump without a limp
- current neuromuscular disease
- history of lower extremity surgery
- any surgery in the last 6 months
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method accuracy and predictability of MDS differentiation between ACL repair and control group Day 1 The investigators hypothesize that MDS will differentiate subjects who have recovered from an ACL reconstruction from a control cohort with accuracy and predictability. Participants will perform drop jumps, sit to stand, and walk on a treadmill in the presence of the micro-Doppler radar. MDS will be obtained for the ACL group as well as control. The micro-Doppler signature projection algorithm (mD-SPA) will then be applied to the data sets showing what percentage of the MDS are successfully classified to the ACL group versus control.
- Secondary Outcome Measures
Name Time Method ability of micro-Doppler radar and deep learning algorithms to automatically produce predictive data Day 1 The investigators hypothesize that by incorporating several deep learning algorithms that can extract high-level deep features automatically through hierarchical architectures, the system will be able to automatically produce predictive data that will not require specialized knowledge to operate. This will allow the system to be used by medical assistants or medical technicians who have no expertise in interpretation of the radar MDS. The investigators will accomplish this by applying numerous deep learning algorithms to the MDS to determine which algorithms most accurately and automatically classify the ACL group from control.
accuracy of MDS differentiation between ACL repair and control groups versus the motion capture system Day 1 The investigators hypothesize that MDS will differentiate between the ACL group versus the control group with greater accuracy compared with the MC system. MDS will show greater sensitivity and specificity for correct classification compared with the gold standard MC. Participants will perform drop jumps, sit to stand, and walk on a treadmill while collecting simultaneously the motion capture data and MDS. The data sets will then be analyzed separately and the sensitivity and specificity of each system will be compared.
Trial Locations
- Locations (2)
Lebanon Valley College
🇺🇸Annville, Pennsylvania, United States
Pennsylvania State University College of Medicine
🇺🇸Hershey, Pennsylvania, United States