Graduation Year

2012

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Mechanical Engineering

Major Professor

Rajiv Dubey, Ph.D.

Committee Member

William Lee, Ph.D.

Committee Member

Craig Lusk, Ph.D.

Committee Member

Kyle Reed, Ph.D.

Committee Member

M. Jason Highsmith, D.P.T.

Committee Member

Stephanie Carey, Ph.D.

Keywords

Inverse Kinematics, Compensatory Motion, Activities of Daily Living (ADL), Range of Motion (RoM), Amputee, Motion Planning

Abstract

This work focuses on the use of 3D motion capture data to create and optimize a robotic human body model (RHBM) to predict the inverse kinematics of the upper body. The RHBM is a 25 degrees of freedom (DoFs) upper body model with subject specific kinematic parameters. The model was developed to predict the inverse kinematics of the upper body in the simulation of a virtual person, including persons with functional limitations such as a transradial or transhumeral amputation. Motion data were collected from 14 subjects: 10 non-amputees control subjects, 1 person with a transradial amputation, and 3 persons with a transhumeral amputation, in the University of South Florida's (USF) motion analysis laboratory.

Motion capture for each subject consisted of the repetition of a series of range of motion (RoM) tasks and activities of daily living (ADLs), which were recorded using an eight camera Vicon (Oxford, UK) motion analysis system. The control subjects were also asked to repeat the motions while wearing a brace on their dominant arm. The RoM tasks consisted of elbow flexion & extension, forearm pronation & supination, shoulder flexion & extension, shoulder abduction & adduction, shoulder rotation, torso flexion & extension, torso lateral flexion, and torso rotation. The ADLs evaluated were brushing one's hair, drinking from a cup, eating with a knife and fork, lifting a laundry basket, and opening a door. The impact of bracing and prosthetic devices on the subjects' RoM, and their motion during ADLs was analyzed.

The segment geometries of the subjects' upper body were extracted directly from the motion analysis data using a functional joint center method. With this method there are no conventional or segment length differences between recorded data segments and the RHBM. This ensures the accuracy of the RHBM when reconstructing a recorded task, as the model has the same geometry as the recorded data. A detailed investigation of the weighted least norm, probability density gradient projection method, artificial neural networks was performed to optimize the redundancy RHBM inverse kinematics. The selected control algorithm consisted of a combination of the weighted least norm method and the gradient projection of the null space, minimizing the inverse of the probability density function. This method increases the accuracy of the RHBM while being suitable for a wide range of tasks and observing the required subject constraint inputs.

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