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.

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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.

The goal of this study was to evaluate the performance of a single video camera system for measuring shoulder rotation during computer work, and to quantify the work and postural space within which the system performs optimally. Shoulder rotation angles calculated using the video system were compared with angles calculated using an active infrared LED three-dimensional motion analysis system while 10 adult volunteers simulated postures for two different trials: typical of normal computer work (freestyle) and with forced shoulder abduction (constrained). Average and absolute errors were calculated to determine the accuracy and precision of the system, respectively, for each trial, for each position, and for both the right and left hands. For the right hand, mean values for the average and absolute errors were –1 and 0 degrees, respectively. Only the absolute error increased significantly to 12 degrees for the constrained posture compared with freestyle. During normal computer work, the video system provided shoulder rotation angle values similar to those of a three-dimensional system, thus making it a viable and simple instrument to use in field studies.