University of Twente Student Theses
Investigating the Performance of Deep Learning Algorithms for Muscle Activation On/Off-Set Detection in Horse Surface Electromyography (sEMG) Data
Rarenco, Andrei (2023) Investigating the Performance of Deep Learning Algorithms for Muscle Activation On/Off-Set Detection in Horse Surface Electromyography (sEMG) Data.
|
PDF
691kB |
| Abstract: | The field of equine sports medicine faces the difficulty of accurately and reliably segmenting surface electromyography (sEMG) signals, specifically identifying the onset and offset of muscle activation. Existing methods, which typically involve human labeling, are labor-intensive and time-consuming. Also, double-threshold methods are used which require quite a lot of tuning. In light of this, the purpose of this study is to investigate the application of advanced machine learning techniques, namely Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for the segmentation of equine surface electromyographic (sEMG) signals. Potential applications of sEMG in equine medicine include performance assessment, injury prevention, and recovery enhancement. Our findings indicate that these models accurately and robustly predict muscle activity onsets and offsets, demonstrating their ability to serve as tools in this field of equine sports medicine. In addition, these findings suggest a novel direction for future re- search, encouraging the investigation and refinement of machine learning methodologies in the field of sEMG signal segmentation. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/96116 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page