Behavioral Modeling of Mechanomyogram Signals Detection and Decomposition System
Hisham Gamal Mohamed Daoud;
Abstract
Chapter Oneintroduces the MMG signals, its origin, history and characteristics. Different detection techniques and comparison between them are illustrated. The motivation for investigating MMG as a control signal, previous work and contribution that demonstrate the potential of MMG as a control signal are mentioned.
Chapter Twogives an overview about the nerves and muscles. The organization of the nervous system and its main components are illustrated. The resting and action potentials are well discussed. The muscle structure, physiology and contraction mechanism are demonstrated. Muscle behaviors in neuromuscular diseases are presented andmyogram techniques such as EMG and MMG are discussed.
Chapter Threepresents the used muscle model with brief explanation regarding its operation and equations. The MEMS based accelerometer is explained with its theory and significance. The specifications and behavioral model of the used MMG sensor are also illustrated.
Chapter Fourintroduces the proposed MMG signals analysis algorithm. Firstly, the experimental protocol is clarified. Then, the concept of Empirical Mode Decomposition (EMD) and Hilbert Spectrum (HS) is presented. After that, the MMG feature to be tested is selected and the decomposition results and spectrums are illustrated and discussed. The decomposition results are statistically analyzed to test the discrimination capability of the proposed algorithm between normal and different pathological cases. Finally, Linear Discriminant Analysis (LDA) classification technique is demonstrated as the classification technique for the system. The classification accuracy is also determined statistically.
Chapter Fiveimplementsthe proposed MMG detection and decomposition system in hardware. The experiment is done on biceps brachii muscle. The detection stage is constructed by using muscle electrical stimulator, MEMS based accelerometer sensor and signal conditioning circuits. The decomposition stage introduces HHT core which is simulated and statistically tested to prove the discrimination capability of the decomposition technique after hardware implementation. The presented HHT core is implemented on FPGA.
Chapter Twogives an overview about the nerves and muscles. The organization of the nervous system and its main components are illustrated. The resting and action potentials are well discussed. The muscle structure, physiology and contraction mechanism are demonstrated. Muscle behaviors in neuromuscular diseases are presented andmyogram techniques such as EMG and MMG are discussed.
Chapter Threepresents the used muscle model with brief explanation regarding its operation and equations. The MEMS based accelerometer is explained with its theory and significance. The specifications and behavioral model of the used MMG sensor are also illustrated.
Chapter Fourintroduces the proposed MMG signals analysis algorithm. Firstly, the experimental protocol is clarified. Then, the concept of Empirical Mode Decomposition (EMD) and Hilbert Spectrum (HS) is presented. After that, the MMG feature to be tested is selected and the decomposition results and spectrums are illustrated and discussed. The decomposition results are statistically analyzed to test the discrimination capability of the proposed algorithm between normal and different pathological cases. Finally, Linear Discriminant Analysis (LDA) classification technique is demonstrated as the classification technique for the system. The classification accuracy is also determined statistically.
Chapter Fiveimplementsthe proposed MMG detection and decomposition system in hardware. The experiment is done on biceps brachii muscle. The detection stage is constructed by using muscle electrical stimulator, MEMS based accelerometer sensor and signal conditioning circuits. The decomposition stage introduces HHT core which is simulated and statistically tested to prove the discrimination capability of the decomposition technique after hardware implementation. The presented HHT core is implemented on FPGA.
Other data
| Title | Behavioral Modeling of Mechanomyogram Signals Detection and Decomposition System | Other Titles | النمذجة السلوكية لنظام استكشاف وتحليل إشارات الرسم الميكانيكى للعضلات | Authors | Hisham Gamal Mohamed Daoud | Issue Date | 2014 |
Recommend this item
Similar Items from Core Recommender Database
Items in Ain Shams Scholar are protected by copyright, with all rights reserved, unless otherwise indicated.