Wearable sensor technology and machine learning for ACL injury diagnosis: an exploratory feasibility study
File(s)
Accepted version
Author(s)
Allott, Natasha
Type
Journal Article
Abstract
Objectives
To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration and angular velocity) combined with machine learning (ML) to identify anterior cruciate ligament (ACL) injuries during clinical knee joint laxity assessments.
Design
Prospective exploratory feasibility study using a case–control design. A feed-forward neural network classifier was trained on time-normalised IMU signals recorded during Lachman and Anterior Drawer tests to distinguish ACL-injured from healthy knees. Model performance was assessed using ten-fold participant-level cross-validation and reported at repetition-wise and subject-wise levels.
Setting
Biomechanics laboratory within a secondary care setting in London, UK. Recruitment occurred through an acute soft-tissue injury management clinic.
Participants
Fifty participants were recruited: 26 healthy controls and 24 individuals with an MRI-confirmed ACL injury (partial or complete) sustained within the current injury episode. Healthy controls contributed 52 uninjured legs and ACL-injured participants contributed 25 injured and 23 contralateral uninjured legs (including 1 bilateral injury). Inclusion criteria for the injured group were acute knee injury, MRI-confirmed ACL tear, and ability to bear weight. Controls were ≥18 years with no history of knee ligament injury. Exclusion criteria included age <18 or >45, chronic joint conditions, non-weightbearing status, pregnancy, allergy to adhesives, or inability to provide informed consent. All participants completed the study.
Interventions
Not applicable. Participants underwent standardised clinical Lachman and Anterior Drawer tests performed by a specialist physiotherapist while IMUs were mounted on the femur and tibia using a custom 3D-printed rig.
Primary and Secondary Outcome Measures
The primary outcome was diagnostic accuracy of an ML model classifying ACL injury status (injured vs healthy) based on IMU-derived linear acceleration and angular velocity (three axes per sensor). Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUROC), and F1 score. Planned model assessments (repetition-wise and subject-wise) were completed as intended. Hyperparameter adjustments were made post hoc to address overfitting when expanding to the full dataset.
Results
Across the full dataset (n=50) the comparative leg model (Method 2) achieved a subject-wise diagnostic accuracy of 81%, sensitivity 83%, specificity 79%, PPV 80%, and NPV 83% using ten-fold participant-level cross-validation. Repetition-wise accuracy was 71%. Discriminative performance was moderate (AUROC 0.773), with an F1 score of 0.889. The individual leg model (Method 1) showed lower performance (subject-wise accuracy 70%; sensitivity 43%; specificity 81%).
Conclusions
IMUs combined with ML show promise as an objective tool for supporting ACL injury diagnosis
during manual laxity tests. ML was used to detect patterns in IMU-derived motion data that are not discernible through manual examination, providing an automated classification of ACL injury status. Although accuracy remains moderate, this feasibility study demonstrates clear potential for wearable-based diagnostics in acute MSK assessment pathways. Larger datasets, refined model architectures, and integration of additional biomechanical features are needed to improve generalisability and clinical applicability. Future research should evaluate real-world implementation, workflow integration, and cost-effectiveness.
Trial Registration
Not a clinical trial; observational study registered with the London–Westminster Research Ethics Committee (REC 22/LO/0464; IRAS 305240).
To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration and angular velocity) combined with machine learning (ML) to identify anterior cruciate ligament (ACL) injuries during clinical knee joint laxity assessments.
Design
Prospective exploratory feasibility study using a case–control design. A feed-forward neural network classifier was trained on time-normalised IMU signals recorded during Lachman and Anterior Drawer tests to distinguish ACL-injured from healthy knees. Model performance was assessed using ten-fold participant-level cross-validation and reported at repetition-wise and subject-wise levels.
Setting
Biomechanics laboratory within a secondary care setting in London, UK. Recruitment occurred through an acute soft-tissue injury management clinic.
Participants
Fifty participants were recruited: 26 healthy controls and 24 individuals with an MRI-confirmed ACL injury (partial or complete) sustained within the current injury episode. Healthy controls contributed 52 uninjured legs and ACL-injured participants contributed 25 injured and 23 contralateral uninjured legs (including 1 bilateral injury). Inclusion criteria for the injured group were acute knee injury, MRI-confirmed ACL tear, and ability to bear weight. Controls were ≥18 years with no history of knee ligament injury. Exclusion criteria included age <18 or >45, chronic joint conditions, non-weightbearing status, pregnancy, allergy to adhesives, or inability to provide informed consent. All participants completed the study.
Interventions
Not applicable. Participants underwent standardised clinical Lachman and Anterior Drawer tests performed by a specialist physiotherapist while IMUs were mounted on the femur and tibia using a custom 3D-printed rig.
Primary and Secondary Outcome Measures
The primary outcome was diagnostic accuracy of an ML model classifying ACL injury status (injured vs healthy) based on IMU-derived linear acceleration and angular velocity (three axes per sensor). Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUROC), and F1 score. Planned model assessments (repetition-wise and subject-wise) were completed as intended. Hyperparameter adjustments were made post hoc to address overfitting when expanding to the full dataset.
Results
Across the full dataset (n=50) the comparative leg model (Method 2) achieved a subject-wise diagnostic accuracy of 81%, sensitivity 83%, specificity 79%, PPV 80%, and NPV 83% using ten-fold participant-level cross-validation. Repetition-wise accuracy was 71%. Discriminative performance was moderate (AUROC 0.773), with an F1 score of 0.889. The individual leg model (Method 1) showed lower performance (subject-wise accuracy 70%; sensitivity 43%; specificity 81%).
Conclusions
IMUs combined with ML show promise as an objective tool for supporting ACL injury diagnosis
during manual laxity tests. ML was used to detect patterns in IMU-derived motion data that are not discernible through manual examination, providing an automated classification of ACL injury status. Although accuracy remains moderate, this feasibility study demonstrates clear potential for wearable-based diagnostics in acute MSK assessment pathways. Larger datasets, refined model architectures, and integration of additional biomechanical features are needed to improve generalisability and clinical applicability. Future research should evaluate real-world implementation, workflow integration, and cost-effectiveness.
Trial Registration
Not a clinical trial; observational study registered with the London–Westminster Research Ethics Committee (REC 22/LO/0464; IRAS 305240).
Date Acceptance
2026-05-26
Citation
BMJ Open
ISSN
2044-6055
Publisher
BMJ Publishing Group
Journal / Book Title
BMJ Open
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted