Paper Info
Title
Collecting a Dataset of Gestures for Skill Assessment in the Field: a beach volleyball serves case study
Abstract
ABSTRACT Activity and gesture recognition from wearable sensors data can be used for skill assessment in order to gauge the capability of a user at performing a task. As many other problem of automatic classification, gesture recognition relies on annotated data for the training of the classification system and to gather a set of gestures for the assessment. The collection of a multi-sensors dataset for this goal can be challenging, especially when it is performed in the field rather than in a more controlled environment such as a laboratory. In this paper, we present the collection of a beach volleyball gestures dataset in the field. The resulting dataset is made publicly available to the community and it includes 585 annotated gestures, collected by 10 users, with 4 wearable inertial sensors per user. In addition, we also provide a list of lessons learnt, suggestions and guidelines to improve future data collections in the field.
Year
DOI
Venue
2021
10.1145/3460418.3479355
Ubiquitous Computing
Keywords
DocType
Citations 
Dataset, Sport, Beach Vollyball, Gesture recognition, Skill assessment
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mathias Ciliberto1336.12
Luis Alejandro Ponce Cuspinera200.34
Daniel Roggen31851137.05