scholarly journals Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

2021 ◽  
Author(s):  
Christopher Beach ◽  
Mingjie Li ◽  
Ertan Balaban ◽  
Alexander J. Casson
Keyword(s):  
Adaptive Filtering ◽  
Inertial Measurement Units ◽  
Motion Artefact ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Artefact Removal

scholarly journals Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

2021 ◽  
Author(s):  
Christopher Beach ◽  
Mingjie Li ◽  
Ertan Balaban ◽  
Alex Casson
Keyword(s):  
Adaptive Filtering ◽  
Electrode Design ◽  
Least Mean Square ◽  
Inertial Measurement Units ◽  
Motion Artefact ◽  
Active Electrode ◽  
Inertial Measurement ◽  
Movement Data ◽  
Measurement Units ◽  
Artefact Removal

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on Inertial Measurement Units (IMUs) to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, IMUs are attached to each individual EEG or ECG electrode to collect more local movement data. This movement data is then used to remove the motion artefact by using Normalised Least Mean Square (NLMS) adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However the performance depends on the quality of the input signal with the algorithm providing better performance on signals with lower signal-to-noise ratios. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new approach compared to widely used, non-parametric, blind-source separation methods, and helps enable \emph{in the wild} EEG recordings to be performed.

scholarly journals Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

2021 ◽  
Author(s):  
Christopher Beach ◽  
Mingjie Li ◽  
Ertan Balaban ◽  
Alex Casson
Keyword(s):  
Adaptive Filtering ◽  
Data Driven ◽  
Electrode Design ◽  
Least Mean Square ◽  
Inertial Measurement Units ◽  
Motion Artefact ◽  
Active Electrode ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Artefact Removal

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

scholarly journals Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

2021 ◽  
Author(s):  
Christopher Beach ◽  
Mingjie Li ◽  
Ertan Balaban ◽  
Alex Casson
Keyword(s):  
Adaptive Filtering ◽  
Electrode Design ◽  
Least Mean Square ◽  
Inertial Measurement Units ◽  
Motion Artefact ◽  
Active Electrode ◽  
Inertial Measurement ◽  
Movement Data ◽  
Measurement Units ◽  
Artefact Removal

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on Inertial Measurement Units (IMUs) to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, IMUs are attached to each individual EEG or ECG electrode to collect more local movement data. This movement data is then used to remove the motion artefact by using Normalised Least Mean Square (NLMS) adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However the performance depends on the quality of the input signal with the algorithm providing better performance on signals with lower signal-to-noise ratios. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new approach compared to widely used, non-parametric, blind-source separation methods, and helps enable \emph{in the wild} EEG recordings to be performed.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

scholarly journals Inertial measurement units to estimate drag forces and power output during standardised wheelchair tennis coast-down and sprint tests

2021 ◽  
pp. 1-19
Author(s):  
Thomas Rietveld ◽  
Barry S. Mason ◽  
Victoria L. Goosey-Tolfrey ◽  
Lucas H. V. van der Woude ◽  
Sonja de Groot ◽  
...  
Keyword(s):  
Power Output ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Drag Forces ◽  
Measurement Units

scholarly journals Measurement of respiratory rate with inertial measurement units

2020 ◽  
Vol 6 (3) ◽  
pp. 237-240
Author(s):  
Simon Beck ◽  
Bernhard Laufer ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller
Keyword(s):  
Air Pollution ◽  
Respiratory Rate ◽  
Frequency Analysis ◽  
Clinical Settings ◽  
Breathing Rate ◽  
Demographic Changes ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Respiratory Parameters ◽  
Measurement Units

AbstractDemographic changes and increasing air pollution entail that monitoring of respiratory parameters is in the focus of research. In this study, two customary inertial measurement units (IMUs) are used to measure the breathing rate by using quaternions. One IMU was located ventral, and one was located dorsal on the thorax with a belt. The relative angle between the quaternion of each IMU was calculated and compared to the respiratory frequency obtained by a spirometer, which was used as a reference. A frequency analysis of both signals showed that the obtained respiratory rates vary slightly (less than 0.2/min) between the two systems. The introduced belt can analyse the respiratory rate and can be used for surveillance tasks in clinical settings.

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