1,721,191 research outputs found
Independent component analysis for extraction of critical features from tongue movement ear pressure signals
No full textSegmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data
No full textThis dataset contains the data used in our manuscript titled "Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data". Data structure is explained in the README.txt file located at the top-level of the dataset. Manuscript title in the README.txt file and contained in the title of the zip file are of a previous working title. Please contact corresponding author Richard B. Woodward for any questions.</span
In-ear microphone speech data recognition using HMMs
No full textMany applications requiring detection and identification of speech while in high noise environments can be commonly found in factory, automobile, aircraft or other settings. In such conditions, collecting speech at other locations than the mouth may lead to speech of better quality than can be obtained at the mouth. Our study focuses on a COTS foamincased in-ear microphone device well suited for multiple users and various environments. We present results obtained with a basic DHMM recognizer implemented on a seven isolated words vocabular
Real-time implementation of a non-invasive tongue-based human-robot interface
No full textReal-time implementation of an assistive human-machine interface system based around tongue-movement ear pressure (TMEP) signals is presented, alongside results from a series of simulated control tasks. The implementation of this system into an online setting involves short-term energy calculation, detection, segmentation and subsequent signal classification, all of which had to be reformulated based on previous off-line testing. This has included the formulation of a new classification and feature extraction method. This scheme utilises the discrete cosine transform to extract the frequency features from the time domain information, a univariate Gaussian maximum likelihood classifier and a two phase cross-validation procedure for feature selection and extraction. The performance of this classifier is presented alongside a real-time implementation of the decision fusion classification algorithm, with each achieving 96.28% and 93.12% respectively. The system testing takes into consideration potential segmentation of false positive signals. A simulation mapping commands to a planar wheelchair demonstrates the capacity of the system for assistive robotic control. These are the first real-time results published for a tongue-based human-machine interface that does not require a transducer to be placed within the vicinity of the oral cavity.<br/
Semi-autonomous micro air and ground vehicle control and video relay through internet and iridium networks
No full textAn orientation reflex for autonomous air vehicles based on a neural model of the cockroach escape response
No full textThis paper investigates a biologically inspired orientation reflex for air vehicles and munitions in the endgame phase flight. The reflex is based upon an artificial neural network model of the American Cockroach’s escape reflex. Guidance commands are output to a Linear Quadratic Regulator (LQR) autopilot that pilots the munition to an optimal path destination and orientation for target strike or obstacle evasion. Simulation and flight test results are presented that demonstrate the reflex’s capability for aerial collision avoidance and instantaneous target strike on evasive targets, even in the presence of false or disruptive sensor data
A decision fusion pattern classification architecture for human-robotic interface
No full textA complete signal processing strategy is presented to detect and precisely recognize tongue movement by monitoring changes in airflow that occur in the ear canal. Tongue movements within the human oral cavity create unique, subtle pressure signals in the ear that can be processed to produce command signals in response to that movement. The strategy developed for the human machine interface architecture includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on 4 tongue movementsof 4 subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the 4 pressure signals can be classified with over 96% classification accuracies across the 4 subjects using the decision fusion classification algorithm
In-ear microphone speech data segmentation and recognition using neural networks
No full textSpeech collected through a microphone placed in front of the mouth has been the primary source of data collection for speech recognition. However, this set-up also picks up any ambient noise present at the same time. As a result, locations which may provide shielding from surrounding noise have also been considered. This study considers an ear-insert microphone which collects speech from the ear canal to take advantage of the ear canal noise shielding properties to operate in noisy environments. Speech segmentation is achieved using short-time signal magnitude and short-time energy-entropy features. Cepstral coefficients extracted from each segmented utterance are used as input features to a back-propagation neural network for the seven isolated word recognizer implemented. Results show that a backpropagation neural network configuration may be a viable choice for this recognition task and that the best average recognition rate (94.73%) is obtained with mel-frequency cepstral coefficients for a two-layer networ
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