SBIR Phase I: Biosensor device for recordation of handwriting

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This Small Business Innovation Research (SBIR) Phase I project addresses the problem of instantaneous digitization of handwriting activity with an objective to verify that the handwriting can be reconstructed from EMG signals recorded from hand muscles. This research will be conducted in several task areas, namely EMG recording in human subjects during handwriting, analysis of EMG records using pattern recognition algorithms to extract the handwriting patterns, reconstruction of handwriting, and displaying the handwriting on a computer. The expectation is that there will be consistent correlation between EMG signals and the handwriting, which will allow the decoding of handwriting patterns and the display of the reconstructed handwriting. It is also expected that the most efficient pattern recognition algorithm to provide accurate handwriting reconstruction will be developed. The proposed research will primarily study two pattern recognition algorithms: linear regression method and Bayesian approach for solving the problem of instantaneous digitizing of handwriting activity.The proposed approach will remove several limitations faced by current technology and should provide a more durable, flexible, accurate, and user friendly product that can be easily adapted to different users. The technology will significantly impact the condition of Carpal Tunnel Syndrome, a common occupational illness being reported among typists. EMG-based fingerless glove can also be used as alternative communication device by disabled people who are not able to talk, or who have hearing problems. The resulting product has many applications in education, medicine, tele-robotics, and can be used by mobile workers. As a wearable computer device, this product will help to improve users image and self esteem. This research project will contribute to the better understanding of muscle interactions. Finally, the handwriting application that will be developed, can become a test bed for analyzing and comparing various pattern recognition algorithms, including traditional statistical algorithms and neural networks, for example Time Lagged Recurrent Networks (TLRN) these algorithms already have numerous applications in various fields.