Haptic Writing Simulator

Fig.1 Haptic writing simulator, customized grip and frame

INTRODUCTION

The aim of the project was to develop a framework called Sympathetic Haptics that will allow a person to faithfully experience another person’s haptic experience in order to support advanced learning and/or training objectives. We have developed modeling techniques and frameworks to transmit sensation of force between humans, so that the trainee may actively follow the expert and hence be able to share the haptic sensation experienced by the expert. The Haptic Writing Simulator uses a virtual environment modeled to simulate the apparent look and feel of writing in the real world. Additional Features incorporated into the simulator include a Customizable Simulation Environment, Database for updating and retrieving haptic data used for real-time analysis of user performance and dynamic controls, Telehaptic Master Slave Configuration used for transmission of haptic data using UDP packets that encapsulate haptic payloads.

Resources

Download the English and Tamil expert hand writing samples from here.

Video Clips

Haptic Writing Simulator

Control System

Customized Frame, Fixtures, Virtual Environment

Current Research Activities

Muscle Behavior Evaluation of the haptic systems based on muscle activity. We performed experiments on two simulators - haptic writing simulator and spring simulator. Comparison were done by studying the muscle behavior with the help of surface ElectroMyoGraph (EMG), during the execution of analogous real and haptic based tasks.	Fig 2. Position of flexor carpi ulnaris and extensor digitorum communis (Left) and the sensors attached to a subjects hand while performing the virtual task (right)
Modeling Development of a novel modeling methodology that has good accuracy, robustness, flexibility and encodes force information involved in writing characters.
	Fig. 3 Bezier Curve	Fig. 4 Cubic Spline	Fig. 5 GLA
Identifying Expert Hand Behavior Analysis was performed to test the consistency of the writing parameters differences in the quantitative variations of the mean values of the writing parameters, the general qualitative pattern of the distribution was same across all the participants indicating that experts’ handwriting strategy is similar (as shown in the figures). The qualitative similarity also implies the automaticity of the experts’ motor behavior. This generic pattern of the handwriting parameters for the different characters can be used as evaluation criteria while training novices
	Fig. 6 Interaction plots on Speed for letter S
	Fig. 7 Interaction plots on Force for Tamil character

People
Dr. T. Kesavadas (Dept. of Mechanical Engineering)
Dr. Ann M. Bisantz (Dept. of Industrial and Systems Engineering)

Ashirwad J Chowriappa

Neeraja Subrahmaniyan

Srimathveeravalli Govind

Xinyan Li

John John Kocherry

Sai Pritham

Sponsors

This material is based upon work supported by the National Science Foundation under Grant No. 0632618

Publications

Srimathveeravalli G, Gourishankar V, Kumar A and Kesavadas T “Experimental Evaluation of Shared Control for Rehabilitation of Fine Motor Skills”, ASME Journal of Computing and Information Science in Engineering, Special Edition on Virtual Reality and Haptics, Vol 9,1 (March 2009)

Chowriappa A, Subrahmaniyan N, Srimathveeravalli G, Bisantz A and Kesavadas T. (2009), Modeling and Defining Expert Handwriting Behavior, in Proc. of IEEE Systems, Man and Cybernetics

John John Kocherry1, Govindarajan Srimathveeravalli1, Ashirwad J Chowriappa1, Thenkurussi Kesavadas1, Gwanseob Shin2, “Improving Haptic Experience through Biomechanical Measurements”, EuroHaptics conference, 2009

Li X, Srimathveeravalli G, Singla P and Kesavadas T, “A Novel and Robust Algorithm to Model Handwriting Skill for Haptic Applications “, in Proc. of IEEE Systems, Man and Cybernetics

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