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

Mace, Michael; Abdullah-Al-Mamun, Khondaker; Naeem, Ali Azzam; Gupta, Lalit; Wang, Shouyan; Vaidyanathan, Ravi

doi:10.1016/j.eswa.2013.03.028

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…It has been previously established, that bioacoustic signals generated through impulsive tongue actions can be noninvasively captured from the ears of an individual [21], [22], [23]. The user expresses their intention through tongue flicks, creating acoustic signals within the ear canals.…”

Section: Tongue-movement Ear Pressure Signalsmentioning

confidence: 99%

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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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Section: Tongue-movement Ear Pressure Signalsmentioning

confidence: 99%

“…4. This framework is described in detail in [23]. The output from the ensemble is based on a majority vote between the two channels of data classified through seven individual base classifier models and implies a total of fourteen heterogeneous members.…”

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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“…To obtain an unbiased decision on movement identification, we will use the majority voting-based ensemble classifier for decision fusion processing. For ensemble classifier, the decisions of the base classifiers are assumed to be autonomous and the final decisions are derived from a mixture of all base system's decisions [ 36 ]. Inherently, in the plurality voting strategy, the ensemble decision picks class w j , if there is

where d t , j is the decision taken by t th base classifier ( t = 1,2,…, T and j = 1,…, C ); C is the number of classes and T is the total number of base classifiers used.…”

Section: Design Of the Neural Network Based Ensemble Classifiers Fmentioning

confidence: 99%

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Islam

Mamun

Deng

2017

Computational Intelligence and Neuroscience

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Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements.

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“…Standardised protocols can be followed for testing MMG signals against known measures of force and EMG during isometric and dynamic contractions to confirm known force/ MMG/EMG relationships. [14][15][16] Reliability of repeated testing on different days is also important to examine, so that the degree of error can be factored into determining true change over time or in response to an intervention. The present study aimed to examine the validity and reliability of MMG signals recorded using novel sensor and signal processing techniques.…”

Section: Introductionmentioning

confidence: 99%

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Meagher

Franco

Turk

et al. 2020

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction: The Mechanical Muscle Activity with Real-time Kinematics project aims to develop a device incorporating wearable sensors for arm rehabilitation following stroke. These will record kinematic activity using inertial measurement units and mechanical muscle activity. The gold standard for measuring muscle activity is electromyography; however, mechanomyography offers an appropriate alterative for our home-based rehabilitation device. We have patent filed a new laboratory-tested device that combines an inertial measurement unit with mechanomyography. We report on the validity and reliability of the mechanomyography against electromyography sensors. Methods: In 18 healthy adults (27-82 years), mechanomyography and electromyography recordings were taken from the forearm flexor and extensor muscles during voluntary contractions. Isometric contractions were performed at different percentages of maximal force to examine the validity of mechanomyography. Root-mean-square of mechanomyography and electromyography was measured during 1 s epocs of isometric flexion and extension. Dynamic contractions were recorded during a tracking task on two days, one week apart, to examine reliability of muscle onset timing. Results: Reliability of mechanomyography onset was high (intraclass correlation coefficient ¼ 0.78) and was comparable with electromyography (intraclass correlation coefficient ¼ 0.79). The correlation between force and mechanomyography was high (R 2 ¼ 0.94). Conclusion: The mechanomyography device records valid and reliable signals of mechanical muscle activity on different days.

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

Cited by 13 publications

References 35 publications

Augmenting neuroprosthetic hand control through evaluation of a bioacoustic interface

Augmenting neuroprosthetic hand control through evaluation of a bioacoustic interface

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

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