About: Real-Time Surf Manoeuvres’ Detection Using Smartphones’ Inertial Sensors

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Real-Time Surf Manoeuvres’ Detection Using Smartphones’ Inertial Sensors
Creator	Bányay, Dániel Ferreira, Patrícia Gomes, Diana Graça, Ricardo Moreira, Dinis
Source	PMC
abstract	Surfing is currently one of the most popular water sports in the world, both for recreational and competitive level surfers. Surf session analysis is often performed with commercially available devices. However, most of them seem insufficient considering the surfers’ needs, by displaying a low number of features, being inaccurate, invasive or not adequate for all surfer levels. Despite the fact that performing manoeuvres is the ultimate goal of surfing, there are no available solutions that enable the identification and characterization of such events. In this work, we propose a novel method to detect manoeuvre events during wave riding periods resorting solely to the inertial sensors embedded in smartphones. The proposed method was able to correctly identify over 95% of all the manoeuvres in the dataset (172 annotated manoeuvres), while achieving a precision of up to 80%, using a session-independent validation approach. These findings demonstrate the suitability and validity of the proposed solution for identification of manoeuvre events in real-world conditions, evidencing a high market potential.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49186-4_22
has license	no-cc
sha1sum (hex)	8d7d4ecfd13fc0bf73a7aeddf395e5e9681f1d27
schema:url	https://doi.org/10.1007/978-3-030-49186-4_22
resource representing a document's title	Real-Time Surf Manoeuvres’ Detection Using Smartphones’ Inertial Sensors
has PubMed Central identifier	PMC7256581
schema:publication	Artificial Intelligence Applications and Innovations
resource representing a document's body	covid:8d7d4ecfd13fc0bf73a7aeddf395e5e9681f1d27#body_text
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