Detecting the direction of listening with the emg signals measured behind ears
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enabling always-available input with muscle-computer interfaces
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Enhancing input on and above the interactive surface with muscle sensing
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
Making muscle-computer interfaces more practical
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Emerging Input Technologies for Always-Available Mobile Interaction
Foundations and Trends in Human-Computer Interaction
| Hi-index | 0.00 |
This paper presents two probabilistic developments for the use with electromyograms (EMGs). First described is a neuroelectric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMGs into individual motor unit action potentials (MUAPs). This Bayesian decomposition method allows for distinguishing individual muscle groups with the goal of enhancing gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture-based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models, which are used to recognize the gestures as they are being performed in real time from moving averages of EMGs. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMGs do not provide an easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups, we present a Bayesian algorithm to separate surface EMGs into representative MUAPs. The algorithm is based on differential variable component analysis, which was originally developed for electroencephalograms. The algorithm uses a simple forward model representing a mixture of MUAPs as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data were obtained using a custom linear electrode array designed for this study