Integration of strain gauge sensor in biceps muscle movement detection using LabView.
Saved in:
| Title: | Integration of strain gauge sensor in biceps muscle movement detection using LabView. |
|---|---|
| Authors: | Kristyawati, Desy1 desy_kristyawati@staff.gunadarma.ac.id, Soerowirdjo, Busono1 busono@staff.gunadarma.ac.id, Christina, Erma Triawati1 ermach@staff.gunadarma.ac.id, Harahap, Robby Kurniawan1 robby_kurniawan@staff.gunadarma.ac.id |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Aug2025, Vol. 15 Issue 4, p3696-3706. 11p. |
| Subjects: | Strain sensors, Biceps brachii, LabVIEW (Computer software), Sports medicine, Machine learning, Data analysis, Muscle injuries, Human activity recognition |
| Abstract: | Muscle injuries caused by sports can have a serious impact on sportsmen, to avoid injuries during sports can be prevented by detecting the wrong movement using a strain gauge sensor attached to the muscle which in this study is devoted to the biceps muscle. The strain gauge will detect muscle movement, and the output generated at the strain gauge will be converted into the form of voltage and current which will be used to be processed using machine learning to get data patterns so that they can be grouped into data patterns of wrong movements and correct movements. The strain gauge movement pattern here is simulated using LabView by using a gauge resistance of 120 Ω, strain configuration quarter bridge 1, gauge factor 2.05, Vex is the excitation voltage given to the Wheatstone bridge is 5 V and the initial voltage -180.08 µV, the strain gauge output pattern is obtained in the form of Excel and with this data can be converted into voltage and current. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
