In this paper we present a method for identifying the static biomechanical parameters of all three upper extremity body segments. The experiment is based on coupling the human arm with an industrial robot which is then used for imposing a specified sagittal plane trajectory. Joint angles and forces in the contact point are collected during this process. An optimization based identification procedure was developed, which assumes the upper extremity model of a 3 Degree of Freedom (3DOF) rigid body planar structure in a closed kinematic chain configuration with the robot. The solution is based on fitting the joint torques calculated from contact forces to those predicted by the inverse dynamic model of the linkage. In order to verify the developed identification procedure the experiment was first performed on a 2DOF mechanical ann with dimensions similar to those of the actual arm. This mechanical model was designed using CAD software that provides an accurate assessment of all necessary dynamic parameters. A suitable low velocity trajectory was imposed into all joints, with very small angular deviations. The outcome of the identification is an estimate of masses and center of gravity (COG) coordinates for the lower arm and palm segments, their products for the upper arm and the passive moments around the measured angle of all joints in the sagittal plane. Finally, the results obtained for the human arm are compared to the literature estimates which are based on average population.
The main goal of this study was an assessment of the shoulder and elbow joint passive moments in the sagittal plane. An industrial robot was used for moving a passive arm very slowlyfrom extension topexion and backwards. The motion was constrained to the sagittal plane, with a three degree of freedom planar structure assumed for the human arm. Comparing the obtainedpassive moments of six young male subjects showed a large adjacent angle dependency. Later on, voluntary muscle joint torques for one particular subject and trial were calculated, based on the acquired passive moment data. The presented methodology is aimed at an application on a rehabilitation robot.
Absrm-This paper presents a method far identifying static parametersOf ail three upper ertremiry segments. An industrial mho1 was wed for moving the subject's arm along B specified sagittal trajectory during which meawrements of the shoulder, elbow and wrist angler and forcer in the contact point were collected. An identification pmeedure war developed in snsiagy with those performed on industrial robots, which assumes the upper extremity model of B 3DOF planar stm~ture in a doled kinematic chain. At first, B witable trajectory had to be obtained in all three joints with appropriate low speed and with very small angle deviations around an operating point. The arm was assumed to be linear in this paint since pansive properties of the arm showed no nanlinaarities far such small angular deviations. Since the imposed movements were very slow 811 dynamic el. feeti could bo discarded, which simplified the mathematical complexity. By programming a suitable trajectory into the rebot controller the ill condltiming problem. could be improved significantly. The algorithm eventually identifier masses and mass center coordinates for the lower arm and palm segments, their pmducti far the upper arm and the operating paint passive momenti (summed e l a~t i~i t l~~ and Coulomb frictions) for all three joints in the sagittal plane. The re~ults are eventually compared to thc litwaturc estimations which are bared an average population.
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