This paper investigated the possibility of estimating 3D lower limb joint kinematics during five popular rehabilitation exercises of the hip and knee joints based on the data collected from a single inertial measurement unit located on the shank. The leg was modeled as a four-degree-of-freedom serial chain, and the relevant joint angles were represented by Fourier series. A least square approach based on the minimization of the difference between the measured and estimated 3D linear accelerations and angular velocities was used to solve the related analytical problem. The approach was validated on ten healthy young volunteers (ten trials each), comparing the proposed approach with the measurements collected through a stereophotogrammetric system. The average root mean square differences between the estimated joint angles and those reconstructed with the stereophotogrammetric system were inferior than 3.2°with correlation coefficients higher than 0.85. Index Terms-Inertial measurement unit, motion analysis, hip and knee rehabilitation.Vincent Bonnet received the B.

This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4° root mean squared error, and segments the motion into repetitions with 96% accuracy.

An important field in physiotherapy is the rehabilitation of gait. A continuous assessment and progress tracking of a patient's ability to walk is of clinical interest. Unfortunately the tools available to the therapists are very time-consuming and subjective. Non-intrusive, small, wearable, wireless sensors can be worn by the patients and provide inertial measurements to estimate the pose of the lower body during walking. For this purpose, we propose two different kinematic models of the human lower body. We use an Extended Kalman Filter to estimate the joint angles and show that a variety of sensors, such as accelerometers, gyroscopes, and motion capture markers, can be used and fused together to aid the joint angle estimate. The algorithm is validated on gait data collected from healthy participants.

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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.