Abstract

This paper looks to explore the challenges faced when producing a set of annotations from videos produced by a pilot study evaluating 24 participants (12 with Parkinson's disease, each accompanied by a healthy volunteer control participant) who are free-living in a house embedded with a platform of sensors. We discuss the outcome measures chosen to annotate from the videos and the controlled vocabularies formulated for this task, the tools and processes, how we intend to achieve standardisation and normalisation of the annotations, and how to improve quality and re-usability of the annotation dataset.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
Subtitle of host publication2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages471-474
Number of pages4
ISBN (Electronic)9781665404242
ISBN (Print)9781665447249
DOIs
Publication statusPublished - 26 Mar 2021

Publication series

Name2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021

Bibliographical note

Funding Information:
This work is supported by the Elizabeth Blackwell Institute for Health Research, University of Bristol, and the Wellcome Trust Institutional Strategic Support Fund, grant code 204813/Z/16/Z, by The Cure Parkinson’s Trust, grant code AW021 and by IXICO plc, grant code R101507-101.

Funding Information:
This work was performed under the SPHERE Next Steps Project funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/R005273/1.

Publisher Copyright:
© 2021 IEEE.

Structured keywords

  • SPHERE

Fingerprint

Dive into the research topics of 'Data labelling in the wild: annotating free-living activities and Parkinson's disease symptoms'. Together they form a unique fingerprint.

Cite this