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Ogdensburg, NY, United States

Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 99.91K | Year: 2011

ABSTRACT: Norconnect, Inc. proposes to develop a unique digital flight glove electronic system which will i) record electromyographic (EMG) signals generated by hand muscles of a pilot or a flight engineer when they move their fingers or hands in the air, ii) recognize recorded EMG signals with accuracy of 99%, and finally, iii) translate recognized signals into computer commands for real-time switch activation, typing of reports, annotation of geo-registered icons, etc. In Phase I of the project, we will utilize our laboratory setup available at Norconnect to develop recognition algorithms for the proposed digital glove electronic system. We will identify and optimize the number of sensors and other system parameters that will assure recognition accuracy of 99%. In Phase II of the project we will develop miniature electronics to be embedded into currently manufactured flight gloves without any essential modification of military specification MIL-G-181188B. We will also fabricate prototype systems and completely optimize their parameters. We expect that, in addition to applications by airline and general aviation pilots, police, border security personnel, road warriors and computer gamers, the proposed digital glove technology will find numerous applications in bio-medical systems that interface human bioelectric activity to external devices. EMG signal recognition, digital flight gloves, gesture control, real-world annotation, hand-wear computer input devices BENEFIT: In addition to applications by airline and general aviation pilots, police, border security personnel, road warriors and computer gamers, the proposed digital glove technology will find numerous applications in bio-medical systems that interface human bioelectric activity to external devices


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 100.00K | Year: 2007

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.


Norconnect Inc | Entity website

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Rupasov V.I.,Norconnect Inc | Lebedev M.A.,Duke University | Erlichman J.S.,St. Lawrence University | Linderman M.,Norconnect Inc
PLoS ONE | Year: 2012

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the β spectral range (13-30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2-3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals. © 2012 Rupasov et al. Source

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