Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities
Today Electromyography (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, muscle weakness, pain, and tremors and in external or wearable robotic exoskeletal systems used in rehabilitation areas. During the recor...
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| Format: | Article |
| Language: | English |
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VSB-Technical University of Ostrava
2022-01-01
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| Series: | Advances in Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://advances.utc.sk/index.php/AEEE/article/view/4437 |
| _version_ | 1797826931366821888 |
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| author | Temel Sonmezocak Serkan Kurt |
| author_facet | Temel Sonmezocak Serkan Kurt |
| author_sort | Temel Sonmezocak |
| collection | DOAJ |
| description | Today Electromyography (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, muscle weakness, pain, and tremors and in external or wearable robotic exoskeletal systems used in rehabilitation areas. During the recording of these signals taken from the skin surface through non-invasive processes, analysis of the signal becomes difficult due to the electrodes attached to the skin not fully contacting, involuntary body movements, and noises from peripheral muscles. In addition, parameters such as age and skin structure of the subjects can also affect the signal. Considering these negative factors, a new adaptive method based on Extended Kalman Filtering (EKF) model for more effective filtering of the muscle signals based on both EMG and MEMS is proposed in this study. Moreover, the accuracy of the parametric values determined by the filter automatically according to the most effective time and frequency features that represent noisy and filtered signals was determined by different machine learning and classification algorithms. It was verified that the filter performs adaptive filtering with 100% effectiveness with Linear Discriminant. |
| first_indexed | 2024-04-09T12:40:13Z |
| format | Article |
| id | doaj.art-db3e9ae43b4d4079b6b07f1b15245ac5 |
| institution | Directory Open Access Journal |
| issn | 1336-1376 1804-3119 |
| language | English |
| last_indexed | 2024-04-09T12:40:13Z |
| publishDate | 2022-01-01 |
| publisher | VSB-Technical University of Ostrava |
| record_format | Article |
| series | Advances in Electrical and Electronic Engineering |
| spelling | doaj.art-db3e9ae43b4d4079b6b07f1b15245ac52023-05-14T20:50:14ZengVSB-Technical University of OstravaAdvances in Electrical and Electronic Engineering1336-13761804-31192022-01-0120331432310.15598/aeee.v20i3.44371167Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle ActivitiesTemel Sonmezocak0Serkan Kurt1Department of Electrical and Electronics Engineering, Faculty of Engineering, Dogus University, Esenkent, Dudullu Osb Mah., Nato Yolu Cad. 265/1, 34775 Istanbul, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, Esenler, 34220 Istanbul, TurkeyToday Electromyography (EMG) and accelerometer (MEMS) based signals can be used in the clinical diagnosis of physical states of muscle activities such as fatigue, muscle weakness, pain, and tremors and in external or wearable robotic exoskeletal systems used in rehabilitation areas. During the recording of these signals taken from the skin surface through non-invasive processes, analysis of the signal becomes difficult due to the electrodes attached to the skin not fully contacting, involuntary body movements, and noises from peripheral muscles. In addition, parameters such as age and skin structure of the subjects can also affect the signal. Considering these negative factors, a new adaptive method based on Extended Kalman Filtering (EKF) model for more effective filtering of the muscle signals based on both EMG and MEMS is proposed in this study. Moreover, the accuracy of the parametric values determined by the filter automatically according to the most effective time and frequency features that represent noisy and filtered signals was determined by different machine learning and classification algorithms. It was verified that the filter performs adaptive filtering with 100% effectiveness with Linear Discriminant.http://advances.utc.sk/index.php/AEEE/article/view/4437accelerometerelectromyographyexoskeletal muscle activityextended kalman filtermachine learning algorithmsignal processing. |
| spellingShingle | Temel Sonmezocak Serkan Kurt Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities Advances in Electrical and Electronic Engineering accelerometer electromyography exoskeletal muscle activity extended kalman filter machine learning algorithm signal processing. |
| title | Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities |
| title_full | Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities |
| title_fullStr | Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities |
| title_full_unstemmed | Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities |
| title_short | Adaptive Filtering of Accelerometer and Electromyography Signals Using Extended Kalman Filter for Chewing Muscle Activities |
| title_sort | adaptive filtering of accelerometer and electromyography signals using extended kalman filter for chewing muscle activities |
| topic | accelerometer electromyography exoskeletal muscle activity extended kalman filter machine learning algorithm signal processing. |
| url | http://advances.utc.sk/index.php/AEEE/article/view/4437 |
| work_keys_str_mv | AT temelsonmezocak adaptivefilteringofaccelerometerandelectromyographysignalsusingextendedkalmanfilterforchewingmuscleactivities AT serkankurt adaptivefilteringofaccelerometerandelectromyographysignalsusingextendedkalmanfilterforchewingmuscleactivities |