Assessment of time–frequency representation techniques for thoracic sounds analysis

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

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A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction

10.20944/preprints202108.0347.v1 ◽

2021 ◽

Author(s):

Tahjid Ashfaque Mostafa ◽

Sara Soltaninejad ◽

Tara L. McIsaac ◽

Irene Cheng

Keyword(s):

Short Term Memory ◽

Wearable Sensors ◽

Freezing Of Gait ◽

Network Architectures ◽

Time Frequency ◽

Frequency Representation ◽

Fog Prediction ◽

Advanced Stages ◽

Short Time ◽

Representation Techniques

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, it’s detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

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Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

Applied Sciences ◽

10.3390/app11062582 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2582

Author(s):

Lucas M. Martinho ◽

Alan C. Kubrusly ◽

Nicolás Pérez ◽

Jean Pierre von der Weid

Keyword(s):

Fourier Transform ◽

Guided Waves ◽

Strain Level ◽

Energy Concentration ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Frequency Representation ◽

The Fourier Transform ◽

Short Time ◽

Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

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Method for designing multi-input system identification signals using a compact time-frequency representation

CEAS Aeronautical Journal ◽

10.1007/s13272-021-00499-6 ◽

2021 ◽

Author(s):

Mathias Stefan Roeser ◽

Nicolas Fezans

Keyword(s):

System Identification ◽

Design Method ◽

Flight Test ◽

Compact Representation ◽

Time Frequency ◽

Stability And Control ◽

Frequency Representation ◽

Aerodynamic Parameters ◽

Definition Of ◽

And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

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Improved Time-Frequency Representation of Multi-Component FM Signals with Compressed Observations

2020 54th Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/ieeeconf51394.2020.9443566 ◽

2020 ◽

Author(s):

Vaishali S. Amin ◽

Yimin D. Zhang ◽

Braham Himed

Keyword(s):

Time Frequency ◽

Fm Signals ◽

Frequency Representation

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Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

Energies ◽

10.3390/en14133725 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3725

Author(s):

Paweł Zimroz ◽

Paweł Trybała ◽

Adam Wróblewski ◽

Mateusz Góralczyk ◽

Jarosław Szrek ◽

...

Keyword(s):

Signal Processing ◽

Acoustic Signal ◽

Processing Technique ◽

Underground Mine ◽

Search And Rescue ◽

Reference Spectrum ◽

Signal Processing Technique ◽

Time Frequency ◽

Communication Device ◽

Frequency Representation

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

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