TY - CHAP

T1 - Using a Seminorm for Wavelet Denoising of sEMG Signals for Monitoring during Rehabilitation with Embedded Orthosis System

AU - Schimmack, Manuel

AU - Hand, Andrea

AU - Mercorelli, Paolo

AU - Georgiadis, Anthimos

N1 - Conference code: 10

PY - 2015

Y1 - 2015

N2 - An orthosis embedded with a surface electromyography (sEMG) measurement system, integrated with metal-polymer composite fibers, was used to monitor the electrical activity of the forearm muscles during movement. The comfortable and noninvasive sEMG system was developed for long term monitoring during rehabilitation. Wavelets were used to denoise and compress the raw biosignals. The focus here is a comparison of the usefulness of the Haars and Daubechies wavelets in this process, using the Discrete Wavelet Transform (DWT) version of Wavelet Package Transform (WPT). A denoising algorithm is proposed to detect unavoidable measured noise in the acquired data, which uses a seminorm to define the noise. Using this norm it is possible to rearrange the wavelet basis, which can illuminate the differences between the coherent and incoherent parts of the sequence, where incoherent refers to the part of the signal that has either no information or contradictory information. In effect, the procedure looks for the subspace characterized either by small components or by opposing components in the wavelet domain. The proposed method is general, can be applied to any low frequency signal processing, and was built with wavelet algorithms from the WaveLab 850 library of the Stanford University (USA).

AB - An orthosis embedded with a surface electromyography (sEMG) measurement system, integrated with metal-polymer composite fibers, was used to monitor the electrical activity of the forearm muscles during movement. The comfortable and noninvasive sEMG system was developed for long term monitoring during rehabilitation. Wavelets were used to denoise and compress the raw biosignals. The focus here is a comparison of the usefulness of the Haars and Daubechies wavelets in this process, using the Discrete Wavelet Transform (DWT) version of Wavelet Package Transform (WPT). A denoising algorithm is proposed to detect unavoidable measured noise in the acquired data, which uses a seminorm to define the noise. Using this norm it is possible to rearrange the wavelet basis, which can illuminate the differences between the coherent and incoherent parts of the sequence, where incoherent refers to the part of the signal that has either no information or contradictory information. In effect, the procedure looks for the subspace characterized either by small components or by opposing components in the wavelet domain. The proposed method is general, can be applied to any low frequency signal processing, and was built with wavelet algorithms from the WaveLab 850 library of the Stanford University (USA).

KW - Active noise filter

KW - Biosignal processing

KW - Noise detection

KW - Wavelet analysis

KW - Wavelet packet transform

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84939504127&partnerID=8YFLogxK

U2 - 10.1109/MeMeA.2015.7145249

DO - 10.1109/MeMeA.2015.7145249

M3 - Article in conference proceedings

AN - SCOPUS:84939504127

SN - 978-1-4799-6476-5

SP - 467

EP - 472

BT - 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE International Symposium on Medical Measurements and Applications - MeMeA 2015

Y2 - 7 May 2015 through 9 May 2015

ER -