Equimetrics -- Applying HAR principles to equestrian activities
September 18, 2024 Β· Entered Twilight Β· π International Workshop on Sensor-based Activity Recognition and Interaction
"No code URL or promise found in abstract"
"Code repo scraped from project page (backfill)"
Evidence collected by the PWNC Scanner
Repo contents: LICENSE, README.md, firmware, software
Authors
Jonas PΓΆhler, Kristof Van Laerhoven
arXiv ID
2409.11989
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
International Workshop on Sensor-based Activity Recognition and Interaction
Repository
https://github.com/limlug/equimetrics
Last Checked
7 days ago
Abstract
This paper presents the Equimetrics data capture system. The primary objective is to apply HAR principles to enhance the understanding and optimization of equestrian performance. By integrating data from strategically placed sensors on the rider's body and the horse's limbs, the system provides a comprehensive view of their interactions. Preliminary data collection has demonstrated the system's ability to accurately classify various equestrian activities, such as walking, trotting, cantering, and jumping, while also detecting subtle changes in rider posture and horse movement. The system leverages open-source hardware and software to offer a cost-effective alternative to traditional motion capture technologies, making it accessible for researchers and trainers. The Equimetrics system represents a significant advancement in equestrian performance analysis, providing objective, data-driven insights that can be used to enhance training and competition outcomes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted