Changes in In Vivo Knee Contact Forces through Gait Modification

Gait modification represents a non-invasive method for reducing knee joint loading in patients with knee osteoarthritis. Previous studies have shown that a variety of gait modifications are effective in reducing the external knee adduction moment. The external knee adduction moment is often used as a surrogate measure of medial compartment force. However, a recent study showed that reductions in the external knee adduction moment do not guarantee reductions in medial compartment loads. Therefore, direct measurement of changes in knee contact force is important for determining the effectiveness of gait modifications. A previous study found that medial thrust gait and walking with hiking poles reduced contact force in a patient with a force-measuring knee replacement. The purpose of this study was to investigate the effects of additional gait modifications (mild crouch, moderate crouch, forefoot strike and bouncy gait) and four configurations of hiking poles on medial and lateral contact forces measured by a force-measuring knee replacement

Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data

Background: Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, and sleep. We applied statistical learning approaches to examine their promise in detecting nonwear time and compared the results with commonly used wear time (WT) algorithms. Methods: Fifteen children, aged 4–17, wore an ActiGraph wGT3X- BT monitor on their hip during overnight polysomnography. We applied Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to classify states of nonwear and wear in triaxial acceleration data. Performance of methods was compared with WT algorithms across two conditions with differing amounts of consecutive nonwear. Clinical scoring of polysomnography served as the gold standard. Results: When the length of nonwear was less than or equal to WT algorithms’ predefined thresholds for consecutive nonwear time, GMM methods yielded improved classification error, specificity, positive predictive value, and negative predictive value over commonly used algorithms. HMM was superior to one algorithm for sensitivity and negative predictive value. When the length of nonwear was longer, results were mixed, with the commonly used algorithms performing better on some parameters but GMM with the greatest specificity. However, all approached the upper limits of performance for almost all metrics. Conclusions: GMM and HMM demonstrated robust, consistently strong performance across multiple conditions, surpassing or remaining competitive with commonly used WT algorithms which had marked inaccuracy when nonwear time periods were shorter. Of the two statistical learning algorithms, GMM was superior to HMM

Simulation of human hands movements using forward kinematics

The human hand is one of the human body extremities that allows to perform numerous movements to accomplish tasks. This paper presents the mathematical model of forward kinematics and the first results of the human hand simulation using computational tools as Matlab, which allows to represent of the movements in 3D. The equations used are based on Denavit-Hartenberg convention, where a homogeneous transformation matrix is determined that relates position values of the end ends of the fingers with the angular position values of the phalanges. The results provide a series of information and recommendations that can be used in the biomechanical study of the hand. The mathematical model serves as complementary information in the gesture recognition process. Helping to solve diverse singularities of possible positions of the fingers when they are not clearly identified in images of intensity for recognition of gestures translated into text or voice