Lower Extremity InjuriesPredKneeNCT07289828
Prediction of Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units
An observational study of Lower Extremity Injuries, run by Technical University of Munich at 1 site in Germany.
Trial summary
This study analyses questionnaires and inertial sensor data from 108 sports science students regarding previous lower extremity injuries, sports activity, and preventive measures, combined with the prospective development of an AI-based prediction algorithm. Read more about this study
- Who is it for?
- Ages 18 to 60 yearsHealthy volunteers can take part
- Conditions the study is for
- Lower Extremity Injuries
- What is being tested?
- IMU Data collection and Questionnaire
- How many people
- 108 participants
- Where
- 1 siteGermany
- Study dates
- 29 Jan 2024to 4 Jun 2024
- 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 60 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Sport students at TUM School of Medicine and Health
Who may be able to join
- Subjectively healthy participants
- Age: >18 years and under 60 years
- German language skills sufficient to follow the exercise instructions and complete the questionnaires
Who cannot join
- Age <18 years or >60 years
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Prediction of Pre-existing Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units
This study analyses questionnaires and inertial sensor data from 108 sports science students regarding previous lower extremity injuries, sports activity, and preventive measures, combined with the prospective development of an AI-based prediction algorithm.
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.
Read more
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.
Read the detailed description
In this study, analysis of questionnaires and inertial sensor data from 108 sports science students is conducted with regard to previous injuries of the lower extremities, their sports activities, and a possible association with performed preventive measures, along with the prospective development of an AI-based prediction algorithm to detect prior injuries of the lower extremities.
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
- Lower Extremity Injuries
- Being tested
- IMU Data collection, Questionnaire
- Keywords
- prediction, ankle injury, knee injury, inertial sensor, artificial intelligence
- Medical subject headings
- Ankle Injuries, Knee Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
IMU Data collectionOther
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).
QuestionnaireOther
On the day of the examination, the test subjects completed a standardized questionnaire on previous injuries, type of sport, sporting activity, and preventive measures.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: IMU Data collection; Other: Questionnaire |
What is being measured
The primary outcome is the main question the study is designed to answer.
IMU data
Time-stamped, unfiltered, device-coordinate-based 3-axis IMU data (Ax, Ay, Az) from four IMUs, placed laterally on both thighs (above the knee joint) and on both ankles (above the lateral malleolus).
Measured: at baseline
Secondary outcomes (3)
questionnaire injuries lower extremity
Participants reported previous injuries and illnesses affecting the lower extremities on a standardized questionnaire.
Measured: Baseline
questionnaire sports activity
Participants indicated their sport and intensity level on a standardized questionnaire.
Measured: Baseline
questionnaire prevention
Participants indicated on a standardized questionnaire whether preventive measures to avoid sports injuries were being implemented.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Dec 2025
- Study start29 Jan 2024
- Primary completion4 Jun 2024
- Study completion4 Jun 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lower Extremity InjuriesStudies of the same condition, open and under way.
- More trials run by Technical University of MunichThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 108 participants (actual)
Sponsor and investigators
- Lead sponsor
- Technical University of Munich
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Christina Valle, Technical University of Munich
- Investigators
- Rüdiger von Eisenhart-Rothe, Prof. Dr. med., Study director, Department of Orthopaedics and Sports Orthopaedics, TUM University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07289828
- Sponsor's protocol ID
- KneeInjuryPred
- Acronym
- PredKnee
- Status last verified
- Dec 2025
- First submitted
- 4 Dec 2025
- Last updated
- 17 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IDP is not permitted due to data protection regulations.
Study at a glance
From the registry record, last updated by the sponsor on 17 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Pre-existing Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units
- ClinicalTrials.gov ID
- NCT07289828
- Acronym / sponsor ID
- PredKnee / KneeInjuryPred
- Conditions
- Lower Extremity Injuries
- Keywords
- prediction, ankle injury, knee injury, inertial sensor, artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 108 participants (actual)
- Lead sponsor
- Technical University of MunichUniversity, hospital or other
- Study start
- 29 Jan 2024 (actual)
- Primary completion
- 4 Jun 2024 (actual)
- Study completion
- 4 Jun 2024 (actual)
- First posted
- 17 Dec 2025 (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.
This study analyses questionnaires and inertial sensor data from 108 sports science students regarding previous lower extremity injuries, sports activity, and preventive measures, combined with the prospective development of an AI-based prediction algorithm.
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
In this study, analysis of questionnaires and inertial sensor data from 108 sports science students is conducted with regard to previous injuries of the lower extremities, their sports activities, and a possible association with performed preventive measures, along with the prospective development of an AI-based prediction algorithm to detect prior injuries of the lower extremities.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| test group | – | Other: IMU Data collection, Other: QuestionnaireAll 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. |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Sport students at TUM School of Medicine and Health
Key inclusion criteria (3 listed)
- Subjectively healthy participants
- Age: >18 years and under 60 years
- German language skills sufficient to follow the exercise instructions and complete the questionnaires
Key exclusion criteria (6 listed)
- Age <18 years or >60 years
- Recent injuries and trauma to the lower extremities (less than 6 months ago)
- Acute malignant disease
- Acute inflammatory disease
- Lack of German language skills
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 |
|---|---|---|
| IMU Data collectionParticipants 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). | Other | test group |
| QuestionnaireOn the day of the examination, the test subjects completed a standardized questionnaire on previous injuries, type of sport, sporting activity, and preventive measures. | Other | test group |
Primary outcome
IMU data
Time frame: at baseline
Time-stamped, unfiltered, device-coordinate-based 3-axis IMU data (Ax, Ay, Az) from four IMUs, placed laterally on both thighs (above the knee joint) and on both ankles (above the lateral malleolus).
3 secondary outcomes
questionnaire injuries lower extremity
Time frame: Baseline
questionnaire sports activity
Time frame: Baseline
questionnaire prevention
Time frame: baseline
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 17 Dec 2025.
- Individual participant dataSharing planned: No. IDP is not permitted due to data protection regulations.
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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.
Rüdiger von Eisenhart-Rothe, Prof. Dr. med. study director
Department of Orthopaedics and Sports Orthopaedics, TUM University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lower Extremity Injuries, run by Technical University of Munich at 1 site in Germany.
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: finished, as of the sponsor's last update on 17 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
IMU data (time frame: at baseline). 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 17 Dec 2025.