Datasets
Klonovs, Juris ; Haque, Mohammad A. ; Krueger, Volker LU
; Nasrollahi, Kamal
; Andersen-Ranberg, Karen
; Moeslund, Thomas B.
and Spaich, Erika G.
(2016)
In SpringerBriefs in Computer Science
p.85-94
- Abstract
Publicly available datasets constitute the ground to evaluate and compare the performance of proposed approaches for monitoring older patients at home. In this chapter, we shed light on the importance of using datasets as a benchmarking tool for comparing various monitoring techniques for detecting the health threats, which we discussed in the previous chapters. The methods, which are tested by using a standard publicly available dataset as a benchmark, are considered to be more reliable and are more likely to be accepted by the scientific community for their claimed results. Therefore, we summarize the references of available datasets, which are relevant to the field of automatic monitoring of older patients.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fb8a32a8-c196-4e04-bd9f-74f710b9650d
- author
- Klonovs, Juris
; Haque, Mohammad A.
; Krueger, Volker
LU
; Nasrollahi, Kamal
; Andersen-Ranberg, Karen
; Moeslund, Thomas B.
and Spaich, Erika G.
- publishing date
- 2016-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Activity recognition dataset, Audiovisual data, Benchmarking, Fall detection dataset, Patient monitoring dataset, Wandering detection dataset
- host publication
- Distributed Computing and Monitoring Technologies for Older Patients
- series title
- SpringerBriefs in Computer Science
- issue
- 9783319270234
- edition
- 9783319270234
- pages
- 10 pages
- publisher
- Springer
- external identifiers
-
- scopus:85021394083
- ISSN
- 2191-5776
- 2191-5768
- DOI
- 10.1007/978-3-319-27024-1_5
- language
- English
- LU publication?
- no
- id
- fb8a32a8-c196-4e04-bd9f-74f710b9650d
- date added to LUP
- 2019-06-28 09:18:23
- date last changed
- 2024-01-16 04:58:12
@inbook{fb8a32a8-c196-4e04-bd9f-74f710b9650d,
abstract = {{<p>Publicly available datasets constitute the ground to evaluate and compare the performance of proposed approaches for monitoring older patients at home. In this chapter, we shed light on the importance of using datasets as a benchmarking tool for comparing various monitoring techniques for detecting the health threats, which we discussed in the previous chapters. The methods, which are tested by using a standard publicly available dataset as a benchmark, are considered to be more reliable and are more likely to be accepted by the scientific community for their claimed results. Therefore, we summarize the references of available datasets, which are relevant to the field of automatic monitoring of older patients.</p>}},
author = {{Klonovs, Juris and Haque, Mohammad A. and Krueger, Volker and Nasrollahi, Kamal and Andersen-Ranberg, Karen and Moeslund, Thomas B. and Spaich, Erika G.}},
booktitle = {{Distributed Computing and Monitoring Technologies for Older Patients}},
issn = {{2191-5776}},
keywords = {{Activity recognition dataset; Audiovisual data; Benchmarking; Fall detection dataset; Patient monitoring dataset; Wandering detection dataset}},
language = {{eng}},
month = {{01}},
number = {{9783319270234}},
pages = {{85--94}},
publisher = {{Springer}},
series = {{SpringerBriefs in Computer Science}},
title = {{Datasets}},
url = {{http://dx.doi.org/10.1007/978-3-319-27024-1_5}},
doi = {{10.1007/978-3-319-27024-1_5}},
year = {{2016}},
}