Charles Jorgensen

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We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies(More)
Sub-auditory speech recognition using electromyogram (EMG) sensors is potentially useful for interfaces in noisy environments, for discreet or secure communications, and for users with speech related disabilities. Past research has shown that a scaled conjugate gradient neural network, using dual tree wavelets for feature transformation, can categorize EMG(More)
This paper describes a prototype system designed to improve first responder situational awareness at emergency scenes. A high degree of situational awareness, both for individual responders and for incident commanders, helps to increase responder safety and improve scene management. The prototype system makes use of a variety of tools and techniques from(More)
This paper presents results of electromyographic-based (EMG-based) speech recognition on a small vocabulary of 15 English words. The work was motivated in part by a desire to mitigate the effects of high acoustic noise on speech intelligibility in communication systems used by first responders. Both an off-line and a real-time system were constructed. Data(More)
1. Extended Abstract Speech intelligibility can be severely degraded by high levels of acoustic noise. Researchers have developed a variety of techniques to minimize the impact of noise, ranging from adaptive noise cancellation to throat microphones. Increasingly, researchers are experimenting with the measurement and analysis of bioelectric signals(More)