Clustering Surface EMG Measurements with Similar Timing Patterns
Gait analysis is a process that qualified health specialists investigate a subject's posture differences in an environment where this person walks. Gait analysis can be enhanced when it is recorded with the presence of an sEMG system. EMG is a diagnostic medical device that can demonstrate musc...
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| Published in: | 2021 29th Signal Processing and Communications Applications Conference (SIU) pp. 1 - 4 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
09-06-2021
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Gait analysis is a process that qualified health specialists investigate a subject's posture differences in an environment where this person walks. Gait analysis can be enhanced when it is recorded with the presence of an sEMG system. EMG is a diagnostic medical device that can demonstrate muscle activations and deactivations. The purpose of this study is to display how muscle activation patterns of a single person alter during his/her locomotive movements in gait. Even healthy individuals exhibit different muscle activation patterns in their gait data. When the subject of the study is a person who has disabilities or problems related to his/her musculoskeletal system, the mentioned fluctuations in the activation patterns are considered to be even higher. The presented method is to gather data through gait analysis and the measuring of muscle movements with the guidance of an sEMG. When these sEMG signals are collected and appropriate signal processing steps are implemented, a trained health professional can recognize the fluctuations of muscle activation patterns of individuals much more easily. The kinematics and Spatio-temporal data of gait is obtained through Xsens wireless 3D motion tracking technology system. Simultaneously, sEMG data is recorded to indicate muscle activations in every single gait. EMG signals are uploaded into the computer and necessary digital signal processing steps are carried out on the raw data recordings. With the help of kinematics data, every gait cycle is determined and EMG data is categorized according to these cycles. In order to normalize EMG signals with respect to maximum contractions, every signal gait is examined separately and the maximum contraction value of each gait cycle is calculated individually. The normalization step is completed by implementing these values to gait cycles. The normalization process is followed by setting a threshold value to understand when the muscle in the examination is activated or deactivated. 25% threshold setting is displayed in this study. The results of the clustering algorithm show 3 types of clusters created by MATLAB regarding how similar or dissimilar the activation patterns of the gait cycles in the uploaded data. |
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| DOI: | 10.1109/SIU53274.2021.9477771 |