Robust real-time identification of tongue movement commands from interferences

Mamun, Khondaker A.; Mace, Michael; Gupta, Lalit; Verschuur, Carl; Lutman, Mark E.; Stokes, María; Vaidyanathan, Ravi; Wang, Shouyan

doi:10.1016/j.neucom.2011.09.018

Cited by 11 publications

(17 citation statements)

References 34 publications

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“…For non-separable data found in the real world in where no hyper plane exists, data's are mapped in to high dimensional space and represented by "Transformed Feature Plane". Kernel functions are used for special class of functions which maps new points in feature space [30,31].…”

Section: Support Vector Machinementioning

confidence: 99%

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Mamun

Alhussein

Sailunaz³

et al. 2017

Future Generation Computer Systems

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Section: Support Vector Machinementioning

confidence: 99%

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Mamun

Alhussein

Sailunaz³

et al. 2017

Future Generation Computer Systems

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“…Figure 1 (bottom left) illustrates the described action set and tongue trajectory during a specific movement. Based on their unique bioacoustic signatures, the prescribed tongue movements are distinguishable from one another as well as normally occurring activity, such as speech, coughing and swallowing [22]. This is due to the motions not typically occurring in daily activity, although they feel natural to execute implicating their repeatability.…”

Section: Tongue-movement Ear Pressure Signalsmentioning

confidence: 99%

A heterogeneous framework for real-time decoding of bioacoustic signals: Applications to assistive interfaces and prosthesis control

Mace

Abdullah-Al-Mamun

Naeem

et al. 2013

Expert Systems with Applications

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“…1 illustrates the described action set and tongue trajectory during a specific movement. Based on their unique bioacoustic signatures, the prescribed tongue movements are distinguishable from one another as well as normally occurring activity, such as speech, coughing and swallowing [24]. This is due to the motions not typically occurring in daily activity, although they feel natural to execute implicating their repeatability.…”

Section: Tongue-movement Ear Pressure Signalsmentioning

confidence: 99%

“…This is based on a short-term energy contour in combination with amplitude and time thresholding. Due to the potential for interfering bioacoustic activity to merge with the signals in real-time a dedicated dichotomous interference rejection (IR) block is implemented prior to inter-action classification [24]. This has been shown to be effective at rejecting higher frequency interference, such as speech and coughing, in both off-line and online environments.…”

Section: Tongue-movement Ear Pressure Signalsmentioning

confidence: 99%

Augmenting neuroprosthetic hand control through evaluation of a bioacoustic interface

Mace

Subbich

Naeem

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The majority of neuroprosthetic interfaces, linking amputee to prosthetic hand, utilise proportional-based control through electromyography (EMG). The clinical translation of these interfaces can be attributed to their relative simplicity, usually requiring only two EMG electrodes to be placed on the flexor and extensor of the forearm. This bi-electrode setup enables opening and closing of hand grasp with an additional manual input used to cycle through the various grip patterns. In recent literature, the main focus has been on higher degree-offreedom control leading to more complicated interfaces which can be considered the main barrier preventing their clinical utility. As such, new methods for grip pattern switching have not been explored with this fieldable strategy lacking any serious attention. In this work, a novel input, augmenting neuroprosthetic hand control, is proposed. This interface is based on bioacoustic signals generated through prescribed tongue movements. We demonstrate that such an interface can provide comparable performance to existing proportional-based systems without requiring any additional movements of the upper extremities.

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Robust real-time identification of tongue movement commands from interferences

Cited by 11 publications

References 34 publications

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

A heterogeneous framework for real-time decoding of bioacoustic signals: Applications to assistive interfaces and prosthesis control

Augmenting neuroprosthetic hand control through evaluation of a bioacoustic interface

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