The application of the integrated kinematic segment to the study of human locomotion, II — Joint dynamics

Abstract

s 655 THE APPLICATION OF THE INTEGRATED KINEMATIC SEGMENT TO THE STUDY OF HUMAN LOCOMOTION, II JOINT DYNAMICS Zvi Ladin and Ge Wu 13iomediral Il:ngincering Department and NeuroMuscular Research Center Boston University, Boston, MA 02215 The calculation of human joint loading is usually based on the non-invasive measurements of the kinematics of a given body segment, and the solution of the ‘inverse dynamics problem’. The kinematics is obtained either by direct position or acceleration measurements. A new approach, called the Integrated Kinematic Segment (IKS) app roach is applied to the study of the three dimensional kinetics of human lower limb during normal walking and running. The IKS is composed of an array of markers for the position and orientation measurement, and a triaxial linear accelerometer and angular rate sensor. Only the knee joint forces and moments are presented in this paper. The results suggest that the use of the IKS is able to increase stibstantially the frequency range in the human joint load estimates, especially at, transients occurring during fast activities. In addition, it is able to provide high quality complete estimates of joint forces and moments at any time or location during locomotion. THE EVALUATION OF COUPLED DYNAMICS IN OBSERVED LOCOMOTION Dwight Me&n, Necip Berme, and Sheldon R. Simon Departments of Mechanical Engineering and Orthopaedic Surgery The Ohio State University, Columbus, OH 43210 USA A method was developed to coupled dynamics) between observe a uantitatively evaluate the interrelationships (also c-&d induced accelerations or body segment kinematic trajectories and the interscgmental resultant joint moments URL.) as well as coriolis, gravitational, and ground reaction loads (GRL) which generate these motions. A numerically based technique derived from the Newton-Euler multibody dynamic simulation method was used to compute the governin connected by sphen .J. . dynamic equations of tlu full human body. The body was modeled by 13 segments JOlntS having a total of 34 degrees of freedom @OF) with 28 DOF which sustain controlled JRL’s from muscles, joint contact, and ca spatial segment kinematic trajectories an 8 suleliigaments. A motion measurement system was used to obtain rhe collect GRL data. This information combined with anthropometric infbrmattlon was used to compute the full set of joint accelerations resulting from all the sources in the model for each time frame in the measured data. An algorithmic techni components calculated in a time frame to determine the sign1 ‘x ue was used to scan the large number of acceleration applymg the method to a normal subject’s gait, man cant contributors to a joint’s kinematic behavior. In wefe an order of magnitude larger than the sum of the individual components of the induced accelerations tot Y. humanr . , I.e. observed, joint accelerations. This showed that normal observe it is a finely balanced activity resulting from the interplay of a number of fanors and chat the same motion can result from multiple combinations of these factors. A COMPARISON BETWEEN KINEMATIC AND IUNETlC METHODS OF SEGMENTAL POWER CALCULATlON IN NORMAL WALKING Andrew W. Smith School of Physical and Health Education, University of Toronto, Toronto, Ontario, Canada M5S 1Al The purpose of the study was to quantify differences in segmental power calculations using (a) direct measurement based on kinetic data and (b) differentiation of segmental energy values. In theory, the two methods should yield the same results. In practice, however, differences do mur. Four subjects each made one visit to the laboratory and had four trials of data recorded from both sides of the body. Analysis included calculating segmental energy components along with joint and muscle powers for each segment. The four trials from each subject were ensemble averaged to obtain intra-subject (ITS) mean and standard deviation curves. Inter-subject (IRS) mean and standard deviation time-histories were calculated by ensemble averaging the ITS mean values. The IRS mean values for each of the two methods were compared using a Pearson product moment correlation (r). The r values were calculated on data from the entire stride (O-100%), and stance (O-60%) and swing (60-100%) phases respectively. The two methods of segmental power calculation yielded similar values, especially during swing phase where 93.1-99.8% of the variance of one method is explained by the other across the three segments. The data for the entire stride, however, contain more unexplained variance, due to differences between methods during the stance phase. This is particularly noticeable for the foot segment. The results of the present study reinforce the strong phasic relationship between kinematic and kinetic methods of segmental power calculation. Caution is advised, however, in interpreting segmental power data based solely on kinematic inputs.