Benzer zamanlama örüntülerine sahip yüzeyel emg ölçümlerinin gruplanmasi

Translated title of the contribution: Clustering surface EMG measurements with similar timing patterns

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Translated title of the contributionClustering surface EMG measurements with similar timing patterns
Original languageTurkish
Title of host publicationSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436496
DOIs
Publication statusPublished - 9 Jun 2021
Externally publishedYes
Event29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 - Virtual, Istanbul, Turkey
Duration: 9 Jun 202111 Jun 2021

Publication series

NameSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings

Conference

Conference29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021
Country/TerritoryTurkey
CityVirtual, Istanbul
Period9/06/2111/06/21

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