loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Przemyslaw Woznowski 1 ; Rachel King 2 ; William Harwin 2 and Ian Craddock 1

Affiliations: 1 University of Bristol, United Kingdom ; 2 University of Reading, United Kingdom

Keyword(s): Activity Recognition, Ontology, Annotation, Video.

Abstract: Human Activity Recognition (AR) is an area of great importance for health and well-being applications including Ambient Intelligent (AmI) spaces, Ambient Assisted Living (AAL) environments, and wearable healthcare systems. Such intelligent systems reason over large amounts of sensor-derived data in order to recognise users’ actions. The design of AR algorithms relies on ground-truth data of sufficient quality and quantity to enable rigorous training and validation. Ground-truth is often acquired using video recordings which can produce detailed results given the appropriate labels. However, video annotation is not a trivial task and is, by definition, subjective. In addition, the sensitive nature of the recordings has to be foremost in minds of the researchers to protect the identity and privacy of participants. In this paper, a hierarchical ontology for the annotation of human activity recognition in the home is proposed. Strategies that support different levels of granularity are p resented enabling consistent, and repeatable annotations for training and validating activity recognition algorithms. Best practice regarding the handling of this type of sensitive data is discussed. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.216.121.55

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Woznowski, P.; King, R.; Harwin, W. and Craddock, I. (2016). A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice. In Proceedings of the International Conference on Internet of Things and Big Data - IoTBD; ISBN 978-989-758-183-0, SciTePress, pages 369-377. DOI: 10.5220/0005932503690377

@conference{iotbd16,
author={Przemyslaw Woznowski. and Rachel King. and William Harwin. and Ian Craddock.},
title={A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - IoTBD},
year={2016},
pages={369-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005932503690377},
isbn={978-989-758-183-0},
}

TY - CONF

JO - Proceedings of the International Conference on Internet of Things and Big Data - IoTBD
TI - A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice
SN - 978-989-758-183-0
AU - Woznowski, P.
AU - King, R.
AU - Harwin, W.
AU - Craddock, I.
PY - 2016
SP - 369
EP - 377
DO - 10.5220/0005932503690377
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic