Wavelets for biomedical signal processing

Akay, Metin; Mello, Carlos A. B.

doi:10.1109/iembs.1997.756888

Cited by 81 publications

(93 citation statements)

References 11 publications

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“…At the end of this step we counted with an activity recognition classifier that could differentiate between different human activities and also detect when a human was present or not in the scene. We present the results of this analysis in our evaluation section [10][11][12][13].…”

Section: Offline Training Methodologymentioning

confidence: 99%

“…After the activity has been recognized, we begin the tracking of the object, which is done through the Pyramidal Lucas-Kanade Opencv [10] implementation. The features which we track are the key points from the Harris Corner Detector; these features were selected since Shi-Tomasi corners provided a non-stable tracking.…”

Section: Classification and Motion Analysis In The Human Recognition mentioning

confidence: 99%

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A Low Cost Outdoor Service Robot

Chávez

Savage

2017

Adv Robot Autom

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Most service robots are simple and domestic, functioning only within Wi-Fi enabled homes. Service robots that do function outdoors are usually expensive and consequently foreign to the general public. To provide service robots to everyone, everywhere, we present Nokovic, a low cost service robot built with recycled parts based on FPGA and radio frequency control that recognizes outdoor and indoor human activity to engage with humans in any environment. By building off recycled pieces, Nokovic reduces the costs to construct the recycled robot. Nokovic also implements a novel low cost human activity recognition system that further helps to reduce the resources needed for the service robot to function anywhere. We establish our human activity recognition system by using a histogram of oriented gradients to create two shape based object classes: bikers and walkers, which our system distinguishes from. We study how our human activity recognition method compares with traditional activity recognition classifiers. Based on our design and study, we reflect on the future of low cost service robots for outdoor and indoor activities. A Low Cost Outdoor Service Robot

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Section: Offline Training Methodologymentioning

confidence: 99%

Section: Classification and Motion Analysis In The Human Recognition mentioning

confidence: 99%

A Low Cost Outdoor Service Robot

Chávez

Savage

2017

Adv Robot Autom

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“…Spectral analysis methods are used for determining the spectral content (distribution of power over frequency) of a time series from a finite set of measurements (Kay & Marple, 1981;Kay, 1988;Proakis & Manolakis, 1996;Stoica & Moses, 1997;Akay, 1998;Akay et al, 1990). The power spectrum is showing abnormal neural activity caused by BBB disruption in the frequency bands.…”

Section: Spectral Nalysis Of Eeg Signalsmentioning

confidence: 99%

“…The PSD estimates represent the changes in frequency with respect to time. The classical methods (nonparametric or fast Fourier transform-based methods), model-based methods (autoregressive, moving average, and autoregressive moving average methods), time-frequency methods (short-time Fourier transform, wavelet transform), eigenvector methods (Pisarenko, multiple signal classification, Minimum-Norm) can be used to obtain PSD estimates of the signals (Kay & Marple, 1981;Kay, 1988;Proakis & Manolakis, 1996;Stoica & Moses, 1997;Akay, 1998;Akay et al, 1990;Übeyli, 2009a;Übeyli, 2009b;Übeyli, 2010). The obtained PSD estimates provide the features which are well defining the signals.…”

Section: Spectral Nalysis Of Eeg Signalsmentioning

confidence: 99%

The Blood-Brain Barrier and Epilepsy

İlbay¹,

Dalçįk²,

Yardımoğlu³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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“…Clements and Hendry, 2001, Akay, 1998and Lio and Vannucci, 2000. A process {X t,N } N t=1 is locally stationary wavelet (LSW) if it admits the mean-square representation:…”

mentioning

confidence: 99%

Efficient computation of the discrete autocorrelation wavelet inner product matrix

Eckley

Nason

2005

Stat Comput

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Discrete autocorrelation (a.c.) wavelets have recently been applied in the statistical analysis of locally stationary time series for local spectral modelling and estimation. This article proposes a fast recursive construction of the inner product matrix of discrete a.c. wavelets which is required by the statistical analysis. The recursion connects neighbouring elements on diagonals of the inner product matrix using a two-scale property of the a.c.wavelets. The recursive method is an O(log(N ) 3 ) operation which compares favourably with the O(N log N ) operations required by the brute force approach. We conclude by describing an efficient construction of the inner product matrix in the (separable) two-dimensional case.

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Wavelets for biomedical signal processing

Cited by 81 publications

References 11 publications

A Low Cost Outdoor Service Robot

A Low Cost Outdoor Service Robot

The Blood-Brain Barrier and Epilepsy

Efficient computation of the discrete autocorrelation wavelet inner product matrix

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