Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3–14
ActiMotus could accurately detect lying, sitting, and standing among children. The software could be improved for classifying walking, running, and stair climbing, particularly among younger children.
Authors
- Amber Beynon (ORCID: https://orcid.org/0000-0002-1302-4429)
- Paul Davey (ORCID: https://orcid.org/0000-0002-2119-8066)
- Andrew L. Rohl (ORCID: https://orcid.org/0000-0003-0038-2785)
- Sarah Stearne
- Juliana Zabatiero (ORCID: https://orcid.org/0000-0002-6815-7544)
- Charlotte Lund Rasmussen (ORCID: https://orcid.org/0000-0003-2193-5019)
- Amity Campbell (ORCID: https://orcid.org/0000-0002-6461-6280)
- Leon Straker (ORCID: https://orcid.org/0000-0002-7786-4128)
- George Thomas (ORCID: https://orcid.org/0000-0001-6908-7974)
- Danica Hendry (ORCID: https://orcid.org/0000-0001-8701-2212)
- Jon Roslyng Larsen (ORCID: https://orcid.org/0000-0003-0373-9305)
Institutions
- The University of Queensland (AU)
- Curtin University (AU)
- ARC Centre of Excellence for the Digital Child (AU)
- National Research Centre for the Working Environment (DK)
Publication Details
- Journal
- Sensors
- Published
- 2024-10-18
- DOI
- https://doi.org/10.3390/s24206705
- Citations
- 2
- Primary Topic
- Children's Physical and Motor Development
- Type
- article
- Field-Weighted Citation Impact
- 1.58
Funders
- Australian Research Council