Prediction of Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units
NCT ID: NCT07289828
Last Updated: 2025-12-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
108 participants
OBSERVATIONAL
2024-01-29
2024-06-04
Brief Summary
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Inertial sensor data were collected during walking and running on a standard 400 m track, with sensors placed on the thighs and ankles, and heart rate recorded via smartwatch. Participants also completed questionnaires on previous injuries, comorbidities, sports activity, and prevention.
The aim is to use the anonymized data to identify gait and running patterns associated with prior knee and ankle injuries using AI analysis, and to correlate these findings with sports activity and preventive measures.
Hypothesis: Prior lower extremity injuries leave specific gait and running patterns detectable by inertial sensors and AI-based analysis.
Detailed Description
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In all participants, inertial sensor data were collected during walking and running on a defined track (5 minutes walking, 5 minutes running, 5 minutes walking on a standard 400 m oval tartan track). Sensors were placed on the lateral aspects of both thighs above the knee joint and on the lateral aspects of both ankles above the lateral malleolus. In addition, participants wore a smartwatch on the left wrist to record heart rate. Furthermore, participants completed questionnaires regarding previous injuries, comorbidities, sports activity, and preventive measures undertaken.
The aim of the current analysis is to utilize the anonymized data from questionnaires and inertial sensors to identify gait and running patterns indicative of previous injuries of the lower extremities (knee and ankle) by means of an AI algorithm, and to correlate these findings with reported sports activities and preventive measures.
Hypothesis: Previous injuries of the lower extremities (particularly of the knee and ankle) result in specific gait and running patterns measurable by inertial sensors, which can be identified through AI-based analysis.
Conditions
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Keywords
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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test group
All participants were sports students at TUM School of Medicine and Health. Of all participants, inertial sensor data were collected during walking and running on a defined track (5 minutes walking, 5 minutes running, 5 minutes walking on a standard 400 m oval tartan track). All participants completed questionnaires regarding previous injuries, comorbidities, sports activity, and preventive measures undertaken.
IMU Data collection
Participants were walking and running while wearing inertial measurement units (IMU) on both legs. The IMUs (MetaMotionS sensor by Mbientlab) where recording at 100Hz (accelerometer and gyroscope) and 25Hz (magnetometer).
Questionnaire
On the day of the examination, the test subjects completed a standardized questionnaire on previous injuries, type of sport, sporting activity, and preventive measures.
Interventions
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IMU Data collection
Participants were walking and running while wearing inertial measurement units (IMU) on both legs. The IMUs (MetaMotionS sensor by Mbientlab) where recording at 100Hz (accelerometer and gyroscope) and 25Hz (magnetometer).
Questionnaire
On the day of the examination, the test subjects completed a standardized questionnaire on previous injuries, type of sport, sporting activity, and preventive measures.
Eligibility Criteria
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Inclusion Criteria
* Age: \>18 years and under 60 years
* German language skills sufficient to follow the exercise instructions and complete the questionnaires
Exclusion Criteria
* Recent injuries and trauma to the lower extremities (less than 6 months ago)
* Acute malignant disease
* Acute inflammatory disease
* Lack of German language skills
* Lack of cardiopulmonary endurance for testing
18 Years
60 Years
ALL
Yes
Sponsors
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Technical University of Munich
OTHER
Responsible Party
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Christina Valle
Senior physician
Principal Investigators
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Rüdiger von Eisenhart-Rothe, Prof. Dr. med.
Role: STUDY_DIRECTOR
Department of Orthopaedics and Sports Orthopaedics, TUM University Hospital
Locations
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TUM University Hospital
Munich, Bavaria, Germany
Countries
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Other Identifiers
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KneeInjuryPred
Identifier Type: -
Identifier Source: org_study_id