Human Movement Datasets: An Interdisciplinary Scoping Review

Movement dataset reviews exist but are limited in coverage, both in terms of size and research discipline. While topic-specific reviews clearly have their merit, it is critical to have a comprehensive overview based on a systematic survey across disciplines. This enables higher visibility of dataset...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:ACM computing surveys 2023-06, Vol.55 (6), p.1-29, Article 126
Hauptverfasser: Olugbade, Temitayo, Bieńkiewicz, Marta, Barbareschi, Giulia, D’amato, Vincenzo, Oneto, Luca, Camurri, Antonio, Holloway, Catherine, Björkman, Mårten, Keller, Peter, Clayton, Martin, Williams, Amanda C De C, Gold, Nicolas, Becchio, Cristina, Bardy, Benoît, Bianchi-Berthouze, Nadia
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Movement dataset reviews exist but are limited in coverage, both in terms of size and research discipline. While topic-specific reviews clearly have their merit, it is critical to have a comprehensive overview based on a systematic survey across disciplines. This enables higher visibility of datasets available to the research communities and can foster interdisciplinary collaborations. We present a catalogue of 704 open datasets described by 10 variables that can be valuable to researchers searching for secondary data: name and reference, creation purpose, data type, annotations, source, population groups, ordinal size of people captured simultaneously, URL, motion capture sensor, and funders. The catalogue is available in the supplementary materials. We provide an analysis of the datasets and further review them under the themes of human diversity, ecological validity, and data recorded. The resulting 12-dimension framework can guide researchers in planning the creation of open movement datasets. This work has been the interdisciplinary effort of researchers across affective computing, clinical psychology, disability innovation, ethnomusicology, human-computer interaction, machine learning, music cognition, music computing, and movement neuroscience.
ISSN:0360-0300
1557-7341
1557-7341
DOI:10.1145/3534970