Using a Seminorm for Wavelet Denoising of sEMG Signals for Monitoring during Rehabilitation with Embedded Orthosis System
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE - Institute of Electrical and Electronics Engineers Inc., 2015. S. 467-472.
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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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 -