Prediction of Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units (PredKnee)

December 4, 2025 updated by: Christina Valle, Technical University of Munich

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.

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Actual)

108

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Germany
    • Bavaria
      • Munich, Bavaria, Germany, 81675
        • TUM University Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Sport students at TUM School of Medicine and Health

Description

Inclusion Criteria:

  • Subjectively healthy participants
  • Age: >18 years and under 60 years
  • German language skills sufficient to follow the exercise instructions and complete the questionnaires

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Other: 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).
Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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).
at baseline

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
questionnaire injuries lower extremity
Time Frame: Baseline
Participants reported previous injuries and illnesses affecting the lower extremities on a standardized questionnaire.
Baseline
questionnaire sports activity
Time Frame: Baseline
Participants indicated their sport and intensity level on a standardized questionnaire.
Baseline
questionnaire prevention
Time Frame: baseline
Participants indicated on a standardized questionnaire whether preventive measures to avoid sports injuries were being implemented.
baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Technical University of Munich

Investigators

  • Study Director: Rüdiger von Eisenhart-Rothe, Prof. Dr. med., Department of Orthopaedics and Sports Orthopaedics, TUM University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 29, 2024

Primary Completion (Actual)

June 4, 2024

Study Completion (Actual)

June 4, 2024

Study Registration Dates

First Submitted

December 4, 2025

First Submitted That Met QC Criteria

December 4, 2025

First Posted (Actual)

December 17, 2025

Study Record Updates

Last Update Posted (Actual)

December 17, 2025

Last Update Submitted That Met QC Criteria

December 4, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KneeInjuryPred

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

IDP is not permitted due to data protection regulations.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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