Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | MUM 2020 |
| Untertitel | 19th International Conference on Mobile and Ubiquitous Multimedia, Proceedings |
| Herausgeber/-innen | Jessica Cauchard, Markus Lochtefeld |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 320-322 |
| Seitenumfang | 3 |
| ISBN (elektronisch) | 9781450388702 |
| Publikationsstatus | Veröffentlicht - Nov. 2020 |
| Veranstaltung | 19th International Conference on Mobile and Ubiquitous Multimedia, MUM 2020 - Virtual, Online, Deutschland Dauer: 22 Nov. 2020 → 25 Nov. 2020 |
Publikationsreihe
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
Winter sports like skiing are becoming increasingly popular for both competitive and recreational activities. To minimize the risk of injury, new innovations in skiing equipment have been developed in recent years. However, unexpected slope conditions can still increase risks during skiing. The static categorisation of ski slopes in winter sports resorts does not take into account dynamic changes of difficulty due to high traffic volumes or sudden weather changes. Up to now, efforts have been made to measure the current conditions via satellite imaging or installations on the slope. However, this requires intervention in nature and causes high maintenance costs. To solve these issues we present our preliminary design of a wearable system to let skiers implicitly measure current slope conditions during their skiing experience. Audio and motion data are recorded from a prototype mounted on a ski boot. We show that the data generated by the prototype can be successfully classified with a neural network. We collected data from a skiing activity to demonstrate our concept and discuss the identified challenges in fitting the proposed approach to winter sports equipment.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Software
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
MUM 2020: 19th International Conference on Mobile and Ubiquitous Multimedia, Proceedings. Hrsg. / Jessica Cauchard; Markus Lochtefeld. Association for Computing Machinery (ACM), 2020. S. 320-322 (ACM International Conference Proceeding Series).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Skiables
T2 - 19th International Conference on Mobile and Ubiquitous Multimedia, MUM 2020
AU - Schrapel, Maximilian
AU - Liebers, Jonathan
AU - Rohs, Michael
AU - Schneegass, Stefan
PY - 2020/11
Y1 - 2020/11
N2 - Winter sports like skiing are becoming increasingly popular for both competitive and recreational activities. To minimize the risk of injury, new innovations in skiing equipment have been developed in recent years. However, unexpected slope conditions can still increase risks during skiing. The static categorisation of ski slopes in winter sports resorts does not take into account dynamic changes of difficulty due to high traffic volumes or sudden weather changes. Up to now, efforts have been made to measure the current conditions via satellite imaging or installations on the slope. However, this requires intervention in nature and causes high maintenance costs. To solve these issues we present our preliminary design of a wearable system to let skiers implicitly measure current slope conditions during their skiing experience. Audio and motion data are recorded from a prototype mounted on a ski boot. We show that the data generated by the prototype can be successfully classified with a neural network. We collected data from a skiing activity to demonstrate our concept and discuss the identified challenges in fitting the proposed approach to winter sports equipment.
AB - Winter sports like skiing are becoming increasingly popular for both competitive and recreational activities. To minimize the risk of injury, new innovations in skiing equipment have been developed in recent years. However, unexpected slope conditions can still increase risks during skiing. The static categorisation of ski slopes in winter sports resorts does not take into account dynamic changes of difficulty due to high traffic volumes or sudden weather changes. Up to now, efforts have been made to measure the current conditions via satellite imaging or installations on the slope. However, this requires intervention in nature and causes high maintenance costs. To solve these issues we present our preliminary design of a wearable system to let skiers implicitly measure current slope conditions during their skiing experience. Audio and motion data are recorded from a prototype mounted on a ski boot. We show that the data generated by the prototype can be successfully classified with a neural network. We collected data from a skiing activity to demonstrate our concept and discuss the identified challenges in fitting the proposed approach to winter sports equipment.
KW - ground classification
KW - skiing
KW - sports
KW - wearable computing
UR - http://www.scopus.com/inward/record.url?scp=85097290125&partnerID=8YFLogxK
U2 - 10.1145/3428361.3432071
DO - 10.1145/3428361.3432071
M3 - Conference contribution
AN - SCOPUS:85097290125
T3 - ACM International Conference Proceeding Series
SP - 320
EP - 322
BT - MUM 2020
A2 - Cauchard, Jessica
A2 - Lochtefeld, Markus
PB - Association for Computing Machinery (ACM)
Y2 - 22 November 2020 through 25 November 2020
ER -