Biomedical subjects

H Flashner

Publications and source records attributed to H Flashner.

4 recordsLinked to original sources

PubMed1989

Parameter optimization model of learning in stepping motion.

In this study we combine the representation of motion by a finite number of hardwired functions with parameter optimization to model learning during a stepping motion. Representation of experimental kinematic data by a finite number of predetermined functions and undetermined coefficients was analyzed. Least squares approximation was used to represent experimental data of stepping motions over obstacles of different heights. Functional relationships between coefficients and obstacles heights were also obtained. Learning of stepping over an obstacle was then formulated as a finite dimensional optimization problem. The pattern of foot path, and joint angles trajectories obtained by this learning model, were then compared to the experimental data. The results of the data fitting analysis and of the optimization process as a model for motion learning, indicate that motion can be adequately represented by a set of hardwired functions, and a finite number of task dependent coefficients.

Humans↗

PubMed1988

Fitting mathematical functions to joint kinematics during stepping: implications for motor control.

The present study extends past work on modeling and control of stepping. The relationship between joint space kinematic data and routine motor control (i.e., open loop) during human stepping is investigated. A model of open loop stepping control using joint kinematics is described. Different functional approximations are employed to simulate experimental joint kinematic data collected on a subject stepping repeatedly over an obstacle. Results indicate that joint kinematics can be characterized by a small number of functions yielding a simple analytical description of open loop motor control. The different basis functions used and their associated coefficients reflected the qualitative behavior of joint trajectories thus allowing flexibility in the formulation of system kinematics. This approach provides a tool to study movement pathologies and movement development by identifying the basis functions governing the kinematics of motion and their associated coefficients. The model presented here is helpful in studying the segmentation of multiarticular movements into their elementary components by analytically modeling the discrete organization of motor behavior.

Foot↗

PubMed1987

Modeling of control and learning in a stepping motion.

In a previous study (Beuter et al. 1986) the authors modeled a stepping motion using a three-body linkage with four degrees of freedom. Stepping was simulated by using three task parameters (i.e., step height, length, and duration) and sinusoidal joint angular velocity profiles. The results supported the concept of a hierarchical control structure with open-loop control during normal operation. In this study we refine the dynamic model and improve the simulation technique by incorporating the dynamics of the leg after landing, adding a foot segment to the model, and preprogramming the complete step motion using cycloids. The equations of the forces and torques developed on the ground by the foot during the landing phase are derived using the Lagrangian method. Simulation results are compared to experimental data collected on a subject stepping four times over an obstacle using a Selspot motion analysis system. A hierarchical control model that incorporates a learning process is proposed. The model allows an efficient combination of open and closed loop control strategies and involves hardwired movement segments. We also test the hypothesis of cycloidal velocity profiles in the joint programs against experimental data using a novel curve-fitting procedure based on analytical rather than numerical differentiation. The results suggest multiobjective optimization of the joint's motion. The control and learning model proposed here will help the understanding of the mechanisms responsible for assembling selected movement segments into goal-directed movement sequences in humans.

Adult↗

PubMed1986

Phase plane modeling of leg motion.

Phase plane analysis of dynamical systems, in which variables are plotted against their time derivatives, has been recently emphasized as a general method for reconstructing system dynamics from data. The purpose of this experiment was to develop a model of leg movement in a stepping task using the phase plane approach. In this model, the leg is represented as a three-body linkage and the motion of the leg is assumed to be planar with four degrees of freedom. Experimental data was collected on one subject stepping six times, using a two dimensional videomotion analysis system with reflective markers placed on the lower limb joints. A computer program able to solve the equations of motion and compute the state of the system for a given task was implemented. This computer program was written to generate the motion of the leg for a given task using inverse kinematics and a preplanned foot path. Foot trajectories with cycloidal, constant acceleration/deceleration and sinusoidal velocity profiles were studied. From the results, an attempt was made to identify the variables which are measured and to determine the motion characteristics during stepping. The preliminary results support the concept of a hierarchical control structure with open-loop control during normal operation. During routine activity there is no direct intervention of the Central Nervous System (CNS). The results support the existence of preprogramming and provide a starting point for the study of the development of control in multiarticulate movements.

Foot↗