Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering

Walle, Axel van de; Naets, Frank; Desmet, Wim

doi:10.1016/j.ymssp.2017.08.032

Cited by 23 publications

(12 citation statements)

References 16 publications

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“…The TVARMA models have already been used to improve non-stationary signal models [ 37 ]. Furthermore, the Kalman filter model has been used extensively in other fields [ 37 , 38 , 39 , 40 , 41 , 42 ], where it is considered an extremely efficient and flexible signal processing tool, and it is also employed in other virtual sensor enforcement [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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“…Note however that Local MOR can be used prior to the Floquet expansion described in Eq. (7). In this case, the modal basis can be over-determined without consequences on the final number of unknown wave amplitudes q + (ω) and q − (ω).…”

Section: Formulation Of a Global Mor Based On The Floquet Expansion O...mentioning

confidence: 99%

“…Time-domain MOR is also critical in the field of virtual sensing, were real-time state/input estimations methods are needed for certain vibroacoustic applications (e.g. see [6,7,8]). Such applications are particularly challenging when the models' outputs require a high level of spatial resolution across a large-scale structure.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale model order reduction scheme for transient modelling of periodic structures

Droz

Boukadia

Desmet

2021

Journal of Sound and Vibration

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“…Furthermore, it has been shown that such a ROM can be used effectively in a state-estimator to obtain a virtual sensor that can accurately estimate the sound pressure and sound intensity in points where no physical sensors are placed [14,15]. Besides state estimation, recent publications on virtual sensing in the field of structural dynamics also show that this technique can work for parameter and input estimation as well [16], although this brings additional challenges.…”

Section: Introductionmentioning

confidence: 99%

Physics-based sound radiation estimation from multiple speakers by combined lumped parameter and reduced-order finite element modeling

Ophem

Desmet

2022

Mechanical Systems and Signal Processing

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Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering

Cited by 23 publications

References 16 publications

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

A multi-scale model order reduction scheme for transient modelling of periodic structures

Physics-based sound radiation estimation from multiple speakers by combined lumped parameter and reduced-order finite element modeling

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