Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue

Fatigue-Aware gaming system for motor rehabilitation using biocybernetic loops.

Esta tesis tiene como objetivo proponer una terapia de rehabilitación complementaria basada en paradigmas de interacción humano-computadora (HCI) que exploran i) Técnicas de rehabilitación virtual, integrando tecnologías de realidad virtual (VR) sofisticadas y (hoy en día) accesibles, ii) sensores fisiológicos de bajo costo, a saber, electromiografía de superficie (sEMG) y iii)sistema inteligente, a través de adaptación biocibernética, para proporcionar una nueva técnica de rehabilitación virtual..

Advanced bioelectrical signal processing methods: Past, present and future approach - Part III: Other biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).Web of Science2118art. no. 606

Klasifikasi Sinyal EMG Dari Otot Lengan Bawah Sebagai Media Kontrol Menggunakan Naive Bayes

Pengguna kursi roda dengan keterbatasan kemampuan dalam mengontrol gerak kursi roda yang digunakannya, akan sangat terbantu bila dibantu dengan tenaga mesin (listrik). Pada umumnya, kursi roda listrik menggunakan joystik dalam pengoperasiannya, sehingga pengguna yang tidak mampu menggunakan joystik perlu alternatif kontrol lain dalam pengoperasiannya. Pada penelitian ini, Sinyal EMG dari Otot lengan bawah diklasifikasi dengan motivasi agar dapat digunakan menjadi alternatif media kontrol. sinyal EMG yang diperoleh dari alat Myo Armband diklasifikasi dengan metode naive bayes. prosesnya dimulai dengan mengumpulkan fitur sinyal sebagai dataset berdasarkan sampling data pada pose gerakan tertentu. dengan melakukan ekstraksi fitur pada domain waktu, yaitu Mean Absolute Value (MAV), Willison Amplitude (WAMP), Root Mean Square (RMS) dan Jumlah Peak (JP). Dari percobaan ini diperoleh hasil evaluasi terhadap 275 raw data bahwa tingkat akurasi evaluasi secara cross-validation dengan 10 kali lipatan pada klasifikasi menggunakan Naive Bayes dengan melakukan eliminasi terhadap instance dataset lebih tinggi dibandingkan dengan hasil evaluasi terhadap dataset penuh yaitu dari tingkat akurasi 86,9% benar, meningkat lebih tinggi menjadi 92.35 % benar ============================================================================ Wheelchair users with limited ability to control wheelchair motion used, will be very helpful when assisted with the power of the engine (electricity). Generally, electric wheelchairs use joysticks in operation, so users who are not able to use joysticks need other alternatives in their operation. In this study, EMG signals from the forearm muscles were classified with motivation to be used as an alternative in automatic control. EMG signals obtained from the myo Arm tool are classified by the naive bayes method. The process begins by collecting the signal features on the dataset based on sampling data on a particular movement. By using the Mean Absolute Value (MAV), Willison Amplitude (WAMP), Root Mean Square (RMS) and number of Peak (JP), it is known from experimental results that the accuracy of classification with Naive Bayes using eliminate the datasetincreased more acurate compared to classification Naive Bayes with full datasetie from the accuracy of 86.9% correct rate, increased to 92.35% correct