ArticleIEEE journal of translational engineering in health and medicine2017
Automated Assessment of Dynamic Knee Valgus and Risk of Knee Injury During the Single Leg Squat.
Article in IEEE journal of translational engineering in health and medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review.Sports medicine (Auckland, N.Z.) · 2023Pooled it
- Acute Effects of Tissue Flossing Coupled with Functional Movements on Knee Range of Motion, Static Balance, in Single-Leg Hop Distance, and Landing Stabilization Performance in Female College Students.International journal of environmental research and public health · 2022Trial
- Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study.JMIR mHealth and uHealth · 2026Article
- Assessing real-world movements using consumer-grade wearable devices: Measuring segment orientations and movement quality.Wearable technologies · 2025Article
- Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning.Journal of medical signals and sensors · 2025Article
- Assessing and Enhancing Movement Quality Using Wearables and Consumer Technologies: Thematic Analysis of Expert Perspectives.JMIR formative research · 2024Article
- A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation.NPJ digital medicine · 2023Article
- A database of physical therapy exercises with variability of execution collected by wearable sensors.Scientific data · 2022Article
- Reliability and Validity of an Inertial Measurement System to Quantify Lower Extremity Joint Angle in Functional Movements.Sensors (Basel, Switzerland) · 2022Article
- Reliability and Differences Between Sexes in Landing Mechanics when Performing the Lateral Bound Test.International journal of sports physical therapy · 2022Article
- Recognizing Full-Body Exercise Execution Errors Using the Teslasuit.Sensors (Basel, Switzerland) · 2021Article
- Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury.International biomechanics · 2021Article
- Sex-dependent differences in single-leg squat kinematics and their relationship to squat depth in physically active individuals.Scientific reports · 2020Article
- MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG.IEEE journal of translational engineering in health and medicine · 2020Article
- Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements.PloS one · 2019Article
- Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures.Journal of rehabilitation and assistive technologies engineeringArticle
- Valgus Control Feedback and Taping Improves the Effects of Plyometric Exercises in Women With Dynamic Knee Valgus.Sports healthArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many clinical assessment protocols of the lower limb rely on the evaluation of functional movement tests such as the single leg squat (SLS), which are often assessed visually. Visual assessment is subjective and depends on the experience of the clinician. In this paper, an inertial measurement unit (IMU)-based method for automated assessment of squat quality is proposed to provide clinicians with a quantitative measure of SLS performance. A set of three IMUs was used to estimate the joint angles, velocities, and accelerations of the squatting leg. Statistical time domain features were generated from these measurements. The most informative features were used for classifier training. A data set of SLS performed by healthy participants was collected and labeled by three expert clinical raters using two different labeling criteria: "observed amount of knee valgus" and "overall risk of injury". The results showed that both flexion at the hip and knee, as well as hip and ankle internal rotation are discriminative features, and that participants with "poor" squats bend the hip and knee less than those with better squat performance. Furthermore, improved classification performance is achieved for females by training separate classifiers stratified by gender. Classification results showed excellent accuracy, 95.7 % for classifying squat quality as "poor" or "good" and 94.6% for differentiating between high and no risk of injury.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.