Human Movement Datasets: An Interdisciplinary Scoping Review

Author:

Olugbade Temitayo1ORCID,Bieńkiewicz Marta2ORCID,Barbareschi Giulia1ORCID,D’amato Vincenzo3ORCID,Oneto Luca3ORCID,Camurri Antonio3ORCID,Holloway Catherine1ORCID,Björkman Mårten4ORCID,Keller Peter5ORCID,Clayton Martin6ORCID,Williams Amanda C De C1ORCID,Gold Nicolas1ORCID,Becchio Cristina7ORCID,Bardy Benoît2ORCID,Bianchi-Berthouze Nadia1ORCID

Affiliation:

1. University College London, London, United Kingdom

2. EuroMov Digital Health in Motion, Univ. Montpellier IMT Mines Ales, Montpellier, France

3. Università di Genova, Genoa, Italy

4. KTH Royal Institute of Technology, Stockholm, Sweden

5. Western Sydney University, Sydney, Australia

6. Durham University, Durham, United Kingdom

7. Department of Neurology, University Medical Center Hamburg-Eppendorf,Germany and Italian Institute of Technology, Genova, Italy

Abstract

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.

Funder

EU Future and Emerging Technologies Proactive Programme H2020

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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