A Dual Mode Human-Robot Teleoperation Interface Based on Airflow in the Aural Cavity

Vaidyanathan, Ravi; Fargues, M.P.; Kurcan, Remzi Serdar; Gupta, Lalit; Kota, Srinivas; Quinn, Roger D.; Lin, Deng-Sung

doi:10.1177/0278364907082612

Cited by 12 publications

(17 citation statements)

References 22 publications

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“…Consequently, tongue movements can be mapped, via ear pressure signals, to generate control signals for HMIs without inserting any device in the oral cavity [4]. We have also demonstrated that spoken words can be recognized from the ear pressure signals [3]. Therefore, speech control signals can also be mapped via ear pressure signals into HMI control signals.…”

Section: Single Channel Classification Examplementioning

confidence: 86%

“…It is not unusual for the resulting dimension to be high especially for signals that are voluntarily generated by humans for human-machine-interface (HMI) control and communication applications. Examples of such signals include speech [1]- [3], ear pressure signals [3], [4], electromyographic signals [5], [6], electroencephalogram (EEG) and event-related potential (ERP) brainwaveforms [6]- [9], and gestures [10], [11]. Due to practical issues related to the data acquisition methods, lack of concentration, discomfort, and fatigue, it may not always be possible to collect enough reliable signals to exceed the dimension of the signal space.…”

Section: Introductionmentioning

confidence: 99%

“…These particular signal classification problems are selected because they are typical of signal classification problems in which the curse of dimensionality is known to occur frequently. Furthermore, the signals are also typical of signals that can be used to control HMIs [3], [4], [11].…”

Section: Introductionmentioning

confidence: 99%

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A Feature Ranking Strategy to Facilitate Multivariate Signal Classification

Gupta

Kota

Murali

et al. 2010

IEEE Trans. Syst., Man, Cybern. C

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A strategy is introduced to rank and select principal component transform (PCT) and discrete transform (DCT) transform coefficient features to overcome the curse of dimensionality frequently encountered in implementing multivariate signal classifiers due to small sample sizes. The criteria considered for ranking include the magnitude, variance, inter-class separation, and classification accuracies of the individual features. The feature ranking and selection strategy is applied to overcome the dimensionality problem which often plagues the implementation and evaluation of practical Gaussian signal classifiers.The applications of the resulting PCT-and DCT-Gaussian signal classification strategies are demonstrated by classifying single-channel tongue-movement ear-pressure signals and multi-channel event-related potentials. Through these experiments, it is shown that the dimension of the feature space can be decreased quite significantly by means of the feature ranking and selection strategy. The ranking strategy not only facilitates overcoming the dimensionality curse for multivariate classifier implementation but also provides a means to further select, out of a rank-ordered set, a smaller set of features that give the best classification accuracies. Results show that the PCT-and DCT-Gaussian classifiers yield higher classification accuracies than those reported in previous classification studies on the same signal sets. Amongst the combinations of the two transforms and four feature selection criteria, the PCT-Gaussian classifiers using the maximum magnitude and maximum variance selection criteria gave the best classification accuracies across the two sets of classification experiments. Most noteworthy is the fact that the multivariate Gaussian signal classifiers developed in this paper can be implemented without having to collect a prohibitively large number of training signals simply to satisfy the dimensionality conditions. Consequently, the classification strategies can be beneficial for designing personalized human-machine-interface (HMI) signal classifiers for individuals from whom only a limited number of training signals can reliably be collected due to severe disabilities.

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Section: Single Channel Classification Examplementioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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A Feature Ranking Strategy to Facilitate Multivariate Signal Classification

Gupta

Kota

Murali

et al. 2010

IEEE Trans. Syst., Man, Cybern. C

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“…Physically impaired individuals clearly may not have the luxury of such freedom of movement. Although several alternatives are currently under development (a brief survey may be found in [1]), no device is currently available today that definitively address all the needs of the patient community at large.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, we have introduced a non-intrusive tongue-movement HMI concept [1][2][3][4][5][6], and shown tongue movements within the oral cavity create unique pressure signals in the ear (dubbed tongue-movement-ear-pressure (TMEP) signals). We have further developed and implemented new pattern classification strategies that have accurately recognized TMEP signals with over 97% accuracy over a range of users [3], hence providing an unobtrusive, completely noninvasive method of controlling peripheral or assist mechanisms through tongue movement.…”

Section: Introductionmentioning

confidence: 99%

A wavelet denoising approach for signal action isolation in the ear canal

Vaidyanathan

Wang

Gupta³

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The goal of this work was to develop and implement a new filtering strategy to denoise acoustic signals in the ear canal resulting from voluntary movement of the tongue (as a method of generating control input), as well as from other active actions, (speech, eating, drinking, smoking), and passive actions (swallowing, adjusting the jaw, physiological activity). The strategy is based on a denoising wavelet shrinkage approach that separates rhythmic bursting activity and white noise representing sustained tonic activity. While past work has addressed the discrimination of voluntary TMEP signals from one-another, no work has addressed acoustic artefact rejection within the ear. The results described here, combined with our past work in isolating critical components of tongue movement ear pressure (TMEP) signals, provide a basis for discriminating voluntary and involuntary actions of the tongue by monitoring pressure in the ear. At this time, the system has worked in real-time for assistive device control.

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