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Completed

NCT Number: NCT07289828

Prediction of 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.

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Key information

Conditions

Lower Extremity Injuries Ankle Injuries Knee Injuries Leg Injuries Wounds and Injuries

Age range

18 year–60 year

Sex eligibility

All sexes

Study type

Observational

Primary location

TUM University Hospital

Munich, Bavaria, 81675, Germany

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

IMU Data collection

Other

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

Other

On the day of the examination, the test subjects completed a standardized questionnaire on previous injuries, type of sport, sporting activity, and preventive measures.

Primary outcomes

  1. 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).

Secondary outcomes

  1. questionnaire injuries lower extremity

    Time frame: Baseline

    Participants reported previous injuries and illnesses affecting the lower extremities on a standardized questionnaire.

  2. questionnaire sports activity

    Time frame: Baseline

    Participants indicated their sport and intensity level on a standardized questionnaire.

  3. questionnaire prevention

    Time frame: baseline

    Participants indicated on a standardized questionnaire whether preventive measures to avoid sports injuries were being implemented.

Sponsors and collaborators

Lead sponsor

Technical University of Munich

Other

Registry information

Official study title

Prediction of Pre-existing Lower Extremity Injuries Using Lower Limb-worn Inertial Measurement Units

Acronym: PredKnee

Important dates

Study start
2024
Primary completion
2024
Study completion
2024
First posted
Dec 17, 2025
Registry last updated
Dec 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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