Exploring Use of Surface Electromyography during Horse Riding
Author
Andersen, Tina
Term
4. term
Education
Publication year
2017
Submitted on
2017-06-08
Pages
19
Abstract
Denne afhandling undersøger overflade-elektromyografi (sEMG), en metode der måler muskelaktivitet med sensorer på huden. Inden for samspillet mellem hest og rytter kan metoden give ryttere adgang til information om hestens muskler, som ellers ikke er tilgængelig. Vi designede og implementerede to prototyper: MyoCollect og HindHelp. MyoCollect er et dataindsamlingssystem, der registrerer sEMG-signaler fra hestens bagben. I tre testsessioner med to forskellige equipager (hest–rytter-par) viser vi, hvordan tre gangarter kan genkendes ved hjælp af statistik og grafanalyse. HindHelp bygger på MyoCollect ved at anvende muskelaktivitetsdata og vise dem for rytteren under springtræning. Vores resultater viser, at det er muligt at måle muskelaktivitet hos heste med sEMG, men det er afgørende, at hesten er klippet, og at sensorerne placeres korrekt. Analysen viser, at de tre gangarter varierer i gennemsnitsniveau, antal toppe og afstanden mellem toppe. Evalueringen af HindHelp tyder på, at sEMG kan være nyttig i en hest–rytter-kontekst, fordi den giver ryttere tidligere utilgængelig viden, men den kræver præcise sensorer og kendskab til hestens muskulatur.
This thesis investigates surface electromyography (sEMG), a method that measures muscle activity using sensors placed on the skin. In horse–rider interaction, it can give riders access to information about their horse’s muscles that would otherwise be unavailable. We designed and implemented two prototypes: MyoCollect and HindHelp. MyoCollect is a data collection system that records sEMG signals from a horse’s hind legs. In three test sessions with two different horse–rider pairs, we show how three gaits can be recognized using statistics and graph analysis. HindHelp builds on MyoCollect by applying the muscle activity data and displaying it to the rider during jumping training. Our results show that measuring muscle activity in horses with sEMG is feasible, but it is crucial that the horse is shaved and the sensors are placed correctly. Our analysis shows that the three gaits differ in average signal level, the number of peaks, and the spacing between peaks. An evaluation of HindHelp suggests that sEMG can be useful in a horse–rider setting by providing previously inaccessible insights, but it demands accurate sensors and knowledge of equine musculature.
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