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.
Many assessment and diagnosis protocols in rehabilitation, orthopedic surgery and sports medicine rely on mobility tests like the Single Leg Squat (SLS). In this study, a set of three Inertial Measurement Units (IMUs) were used to estimate the joint pose during SLS and to classify the SLS as poor, moderate or good. An Extended Kalman Filter pose estimation method was used to estimate kinematic joint variables, and time domain features were generated based on these variables. The most important features were then selected and used to train Support Vector Machine (SVM), Linear Multinomial Logistic Regression, and Decision Tree classifiers. The results of feature selection highlight the importance of the ankle internal rotation (IR) angle in classifying SLS. Classification results on a human motion dataset achieved an accuracy of 98% for the two-class problem using SVM, while for 3 class classification, the maximum accuracy was 73% using Decision Tree.
Introduction
Inertial measurement units have been proposed for automated pose estimation
and exercise monitoring in clinical settings. However, many existing methods
assume an extensive calibration procedure, which may not be realizable in
clinical practice. In this study, an inertial measurement unit-based pose
estimation method using extended Kalman filter and kinematic chain modeling
is adapted for lower body pose estimation during clinical mobility tests
such as the single leg squat, and the sensitivity to parameter calibration
is investigated.
Methods
The sensitivity of pose estimation accuracy to each of the kinematic model
and sensor placement parameters was analyzed. Sensitivity analysis results
suggested that accurate extraction of inertial measurement unit orientation
on the body is a key factor in improving the accuracy. Hence, a simple
calibration protocol was proposed to reach a better approximation for
inertial measurement unit orientation.
Results
After applying the protocol, the ankle, knee, and hip joint angle errors
improved to
, and
, without the need for any other calibration.
Conclusions
Only a small subset of kinematic and sensor parameters contribute
significantly to pose estimation accuracy when using body worn inertial
sensors. A simple calibration procedure identifying the inertial measurement
unit orientation on the body can provide good pose estimation
performance.
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