Many clinical assessment protocols 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. Developing a reliable automatic human motion tracking and assessment system can improve the accuracy of SLS clinical assessments and provide objective results that can be tracked and monitored over time to guide rehabilitation and determine an individual's response to an intervention. In this study, 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.

First, an automated pose estimation method is applied to SLS motion data. A set of three IMUs is used to estimate the joint angles, velocities and accelerations of the squatting leg. To tackle noisy sensor measurements and gyro drift, a 7 degree of freedom (DOF) kinematic model of the lower leg was applied together with a constant acceleration assumption to approximate the angular velocity and linear acceleration at each sensor location. The kinematic model predictions of the angular velocity and linear acceleration and sensor measurements were fused via an Extended Kalman Filter (EKF). The position, velocity, and acceleration of each DOF were defined as the states to be estimated by the EKF. The pose estimation results showed successful extraction of joint angles with an average RMS error of 3.2 degrees, 5.5 degrees, 7 degrees compared to joint angles estimated from motion capture for the ankle, knee, and hip joints, respectively. For this estimation, the required parameters for the kinematic model, including information about the sensor placement and orientation as well as the kinematic link lengths, were extracted from the marker data.

However, in clinical applications of the proposed method, when marker data is not available, these parameters need to be measured. Measuring these parameters is time consuming in the clinical setting, which limits application of IMUs for clinical purposes. With the motivation to make this procedure easier and faster, a method for approximating the parameters using placement assumptions and body measures was described. A sensitivity analysis was performed to detect those parameters which most affect pose estimation accuracy. The sensitivity analysis results revealed that sensor orientation is the most critical factor for accurate pose estimation. In this thesis, a simple and easy to use method is proposed for sensor orientation calibration, based on a systematic placement of sensors and using gyroscope information for orientation estimation. This protocol was evaluated experimentally and pose estimation error with approximated parameters before and after applying the calibration protocol were compared. The comparison results showed that the estimate of the sensor orientation increases the pose estimation accuracy by 6.5 degrees for the knee joint angle and with an average of 1.8 degrees for other joints without the need for time consuming calibration.

In the second part of the thesis, an algorithm for automated assessment of the SLS in terms of dynamic knee valgus and risk of knee injury is developed. After applying the pose estimation algorithm to IMU data of SLS motions, the estimated time series data of joint angles, velocities and accelerations for consecutive squats were segmented into individual squat repetitions. Statistical time domain features were generated from each repetition. The most informative features were selected using a combination of 18 feature selection techniques. Six common classifiers in including SVM, Linear Multinomial Logistic Regression, Decision Tree, Naive Bayes, K Nearest Neighborhood, and Random Forests were applied to the full dimensional data, the subset of selected features, and extracted features by supervised principal component analysis. The proposed approach was evaluated in two trials. First, a pilot study was conducted on a small dataset, followed by analysis on a larger clinical data set, collected by our clinical collaborator. For the clinical study, a dataset of SLS performed by healthy participants was collected and labelled by three expert clinical raters using two different labeling criteria:​ "observed amount of knee valgus" and "overall risk of injury". Labels included "good", "moderate", and "poor" squat quality or "high risk", "mild to moderate risk", and "no risk" of injury. Feature selection 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 classifi cation performance was achieved by training separate classifi ers strati ed by gender. Classifi cation results showed excellent accuracy, 93.1% for classifying squat quality as "poor" or "good" and 95.3% for differentiating between high and no risk of injury.