Biomedical subjects

Kjartan Halvorsen

Publications and source records attributed to Kjartan Halvorsen.

3 recordsLinked to original sources

PubMed2005

A new device for controlled eccentric overloading in training and rehabilitation.

The aim of this work was to evaluate a device that allows for eccentric overload to be applied under controlled and safe conditions and it is applicable in exercises commonly used in training and rehabilitation. The machine contains a barbell, which is lowered and raised by a motor, following a predetermined velocity profile. It is capable of handling heavy loads (>500 kg) and is instrumented with a sensor to measure the velocity of the barbell and two scales to measure the vertical component of the ground reaction force. The velocity recordings of the built-in displacement sensor were found to correspond well with those obtained using a motion-capture system. Applying known weights on each scale demonstrated linearity with respect to magnitude and independence regarding location of application. The velocity of the barbell was found to be dependent on the load on the barbell and on the resisting force produced by the individual training in the machine. The combined man-machine reliability was tested using a group of habitually active males (n = 13, 28-55 years) performing squats. Peak voluntary resisting force and position at peak resistance were recorded on two occasions, showing no significant differences and a coefficient of variation of 9% and 22%, respectively. Preliminary observations from training in the machine have been positive both for increasing performance in top athletes and for causing pain relief in patients with diffuse knee problems. The possibility of feedback of the force under each foot makes individual dosage of training load possible, which is valuable, e.g. in rehabilitation of a unilateral injury.

Adult↗

PubMed2005

Using an extended kalman filter for rigid body pose estimation.

Rigid body pose is commonly represented as the rigid body transformation from one (often reference) pose to another This is usually computed for each frame of data without any assumptions or restrictions on the temporal change of the pose. The most common algorithm was proposed by Söderkvist and Wedin (1993, "Determining the Movements of the Skeleton Using Well-configured Markers," J. Biomech., 26, pp. 1473-1477), and implies the assumption that measurement errors are isotropic and homogenous. This paper describes an alternative method based on a state space formulation and the application of an extended Kalman filter (EKF). State space models are formulated, which describe the kinematics of the rigid body. The state vector consists of six generalized coordinates (corresponding to the 6 degrees of freedom), and their first time derivatives. The state space models have linear dynamics, while the measurement function is a non-linear relation between the state vector and the observations (marker positions). An analytical expression for the linearized measurement function is derived. Tracking the rigid body motion using an EKF enables the use of a priori information on the measurement noise and type of motion to tune the filter. The EKF is time variant, which allows for a natural way of handling temporarily missing marker data. State updates are based on all the information available at each time step, even when data from fewer than three markers are available. Comparison with the method of Söderkvist and Wedin on simulated data showed a considerable improvement in accuracy with the proposed EKF method when marker data was temporarily missing. The proposed method offers an improvement in accuracy of rigid body pose estimation by incorporating knowledge of the characteristics of the movement and the measurement errors. Analytical expressions for the linearized system equations are provided, which eliminate the need for approximate discrete differentiation and which facilitate a fast implementation.

Algorithms↗

PubMed2003

Bias compensated least squares estimate of the center of rotation.

It is shown that a recently published least squares method for the estimation of the average center of rotation is biased. Consequently, a correction term is proposed, and an iterative algorithm is derived for finding a bias compensated solution to the least squares problem.The accuracy of the proposed bias compensated least squares method is compared to the previously proposed least squares method by Monte-Carlo simulations. The tests show that the new method gives a substantial improvement in accuracy.

Algorithms↗