Graduation Year


Document Type




Degree Granting Department

Mechanical Engineering

Major Professor

Rajiv V. Dubey, Ph.D.

Committee Member

Glen Besterfield, Ph.D.

Committee Member

Daniel Hess, Ph.D.

Committee Member

Shuh-Jing Ying, Ph.D.

Committee Member

Wilfrido A. Moreno, Ph.D.

Committee Member

A.N.V. Rao, Ph.D.


Rehabilitation, Hidden Markov Model, Motion Intention Recognition, Virtual Fixture, Skill Learning, Therapy


This dissertation addresses the development of a telemanipulation system using intelligent mapping from a haptic user interface to a remote manipulator to assist in maximizing the manipulation capabilities of persons with disabilities. This mapping, referred to as assistance function, is determined on the basis of environmental model or real-time sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. This is particularly useful when the user has restricted range of movements due to certain disabilities such as muscular dystrophy, a stroke, or any form of pathological tremor.

In telemanipulation system, assistance of variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity. Conventionally, these assistances are based on the environment information, without knowing user's motion intention. In this dissertation, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture orthogonal to the path is applied. Similarly, if the task is to align the end-effector with a target, an attractive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM) is developed.

This dissertation describes the HMM based skill learning and its application in a motion therapy system in which motion along a labyrinth is controlled using a haptic interface. Two persons with disabilities on upper limb are trained using this virtual therapist. The performance measures before and after the therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented.

The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job related tasks.