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

Technical University of Munich

Status: Completed

Conditions: Lower Extremity Injuries

Treatments: Other: IMU Data collection; Other: Questionnaire

Study type: Observational

Funder types: Other

Identifiers

NCT07289828

KneeInjuryPred

Details and patient eligibility

About

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.

Full 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.

Enrollment: 108 patients

Sex: All

Ages: 18 to 60 years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

108 participants in 1 patient group

test group

Description:

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.

Treatment:

Other: Questionnaire

Other: IMU Data collection