This paper discusses the possibility of improving eye-hand coordination in children diagnosed with this problem, using a robotic mapping from a haptic user interface to a virtual environment. The goal is to develop, implement and refine a system that will assess and improve the eye-hand coordination and grip strength in children diagnosed with having problems with it. A detailed analysis of patters (e.g., labyrinths, letters and angles) was conducted which would yield the greatest benefit in terms of assessment of coordination and strength issues as well as in training. Support algorithms (position, force, velocity, inertia and viscosity) were also developed and incorporated into the tasks to assist the user's movements. The evaluate performance (given by % accuracy and time) of the executed tasks; a sophisticated evaluation function was designed based on image analysis and edge detection algorithms. A Fuzzy Logic-based function that takes the accuracy and time results and makes a suggestion in the next task to be performed is also described in this paper. This paper also presents the results of a study implemented at the Motor Development Clinic at Cal Poly Pomona. The purpose of this study was to test the functionality of the haptic tasks, evaluation function and Fuzzy Logic-based decision making algorithm.

Our goal is to develop an automated assessment and training procedure for children with eye-hand coordination problem. An automated assessment system is expected to reduce the burden and the associated cost of having a trained professional present at any assessment, or training session. The intelligent decision support system will be based upon a fuzzy automaton. By using qualitative (fuzzified) data from the previous test the system will make a decision on the complexity of the next test to be performed. A set of assessment tests, commonly used by occupational therapists, were chosen to implement the various functions using force, inertia and viscosity effects. A test bed will be used for these tasks that consists of a six-degree-of-freedom force-reflecting haptic interface device called PHANToM along with the GHOST SDK Software, and the Intelligent Decision Support System software

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, 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 is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.

We discuss the possibility of improving eyehand coordination in children diagnosed with this problem, using a robotic mapping from a haptic user interface to a virtual environment. Our goal is to develop an assessment and training procedure that will result in improving handwriting taking the advantage of the force feedback provided by the haptic device. Force feedback can be used to guide the subject's hand in a predetermined trajectory when he/she is unable to move in response to visual feedback. We also incorporate the inertia and viscosity effects to decrease the tremor in the hand as well as to stimulate the muscles involved in the task of holding a pencil (known as facilitation technique in the occupational therapy field). The robotic system is operated within ranges established by baseline data for accuracy of eye-hand coordination and grip strength. A set of assessment tests, commonly used by occupational therapists, were chosen to implement the various functions using force, inertia and viscosity effects. The test bed used for these tasks consisted of a six-degree-of-freedom force reflecting haptic interface device, PHANToM with the GHOST SDK Software. Preliminary data obtained from two children diagnosed with eye-hand coordination problems show that the assistance provided, improved their ability to execute and complete the tasks, suggesting that the system could be used for assessment and training purposes. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit and measures the impact on the occupation of handwriting. Furthermore, a real application of this system could be implemented in the future, by identifying specific job tasks that may be made accessible through a desktop in classrooms by the aforementioned haptic system.

This paper describes the Hidden Markov Model (HMM) based skill learning and its application in a motion therapy system using a haptic interface. A relatively complex task, requiring motion along a labyrinth is used. A normal subject executes this task for a number of times and the best trajectory is selected as the learned skill, which is considered as a virtual therapist who can train persons with disabilities to complete the task. Two persons with disabilities on upper limb (cerebral palsy) were trained using the virtual therapist. The performance before and after therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented in this paper. This labyrinth can be used as a physical therapy for upper limb coordination, tremor reduction and motion control capabilities.

In telemanipulation systems, assistance of virtual fixture can improve manipulation capability and dexterity. This assistance provides aids not only for path following, but also for reaching target and avoiding obstacles. Conventionally, these assistances are based on the environment information, without knowing user's motion intention. In this paper, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a hard virtual fixture orthogonal to the path is applied. Or if the task is to position a target, an attractive force filed is produced. In order to successfully recognize user's motion intention, a hidden Markov model (HMM) is developed to classify human actions, such as following path, positioning target and avoiding obstacles. The algorithm is tested on the simulation platform.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing occupational therapy tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or 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. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. This work is the continuation of the authors' previous work at the ICRA'02 [Pernalete, N, et al, 2002], in which only results of fully able individuals were presented. Two motion-impaired users with severe forms of muscular dystrophy and spinal cord injure, volunteered to participate in the experiments and their results are presented in this article. The results demonstrated that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals as well as they did for the fully-able persons. 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.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing vocational tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or 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. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks. 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.

This paper describes several assistance approaches to use complex and variable telerobotic mapping, such that limited and irregular inputs from an individual with disabilities are mapped into complex motions. The approach presented implies several forms of assistance functions in which the human input is enhanced rather than superseded by the computer. Some complex tasks in the slave manipulator can be implemented through simple and restricted movements on the master's side coming from a person with restricted dexterity. Three different tasks were chosen to implement the assistance functions designed to augment the teleoperation performance of motion-impaired users. A comparison is made between the implementation of the constraints on the master and the slave sides of the system. The system used consisted of the Phantom haptic device and the RRC (Robotics Research Corporation) seven-degree-of-freedom manipulator. The results demonstrated that the forms of assistance provided not only allowed users to perform complex tasks, but also, reduced the execution times and increased the operation accuracy of the chosen tasks.

Radioactive tank waste remediation and decontamination and decommissioning (D&D) of contaminated Department of Energy (DOE) facilities, and other nuclear cleanup tasks require extensive remote handling technologies. The unstructured nature of these tasks and limitations of the current sensor and computer decision-making technologies prohibit the use of completely autonomous systems for remote manipulation. This paper presents a new methodology in which model-based computer assistance is incorporated into human controlled teleoperator systems. This approach implies a form of assistance function in which the human input is enhanced rather than superseded by the computer. A specific task of cutting a pipe with a saw is chosen as an example to demonstrate the implementation of the assistance functions in D&D size reduction tasks and the results are presented.

There has been increasing attention on human-machine cooperative teleoperation due to limited capabilities of autonomous robots. A method of sensor and model assisted teleoperation using variable velocity mapping is presented. The application of variable velocity mapping to the execution of Fitts tasks is described, and comparisons are made with traditional teleoperation. Application to a practical task of docking is also described.