scholarly journals Kalman Filter Based Electromyographic Signal Suppression of Real-Time ECG Signal

2018 ◽  
Author(s):  
Meng Chen ◽  
Yizhou Zhong ◽  
Huaiyu Zhu ◽  
Yun Pan
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ecg Signal ◽  
Signal Suppression ◽  
Electromyographic Signal

DENOISING AND REMOTE MONITORING OF ECG SIGNAL WITH REAL-TIME EXTENDED KALMAN FILTER IN A WEARABLE SYSTEM

2015 ◽  
Vol 27 (01) ◽  
pp. 1550009 ◽  
Author(s):  
Osman Ozkaraca ◽  
Inan Guler
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Single Channel ◽  
Direct Access ◽  
Ecg Signal ◽  
Extended Kalman Filtering ◽  
Real Time System ◽  
Biomedical Device ◽  
Long Term Follow Up ◽  
Conductive Textile

In this paper, a prototype of wearable and wireless electrocardiography (ECG) monitoring system is developed and implemented on DSP and PDA. We present a real-time extended Kalman filtering framework for extracting motion and electromyography (EMG) artifacts from a single-channel ECG in wearable systems as different from other offline studies. Realized prototype is a good example for the usage of the Kalman filter in biomedical real-time system. The average SNR advancement of 9.1430 dB was achieved for denoising, which is average 1 dB more than the other methods such as MABWT, EKF2 by using MIT-BIH database. Additionally, the usability and performances of conductive textile electrodes were evaluated with disposable Ag – AgCl electrodes by using daily activities. A novel textile electrode gave approximately 25.23% better results compared to Ag – AgCl electrodes. Also, UDP, TCP and Web Socket communication protocols have been tested. UDP has been the fastest method for the ECG signal transferring from the patient to the doctor. At the same time, a method is proposed for direct access to the patient by the doctor. The results illustrate that this type of system will submit highly ergonomic solutions among biomedical device technologies. In addition, the usage of such kinds of systems is foreseen for requiring long-term follow-up and disorders.

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

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