Modeling and forecasting of at home activity in older adults using passive sensor technology.
autoregressive
binary series
home sensing
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
15 10 2022
15 10 2022
Historique:
revised:
06
06
2022
received:
08
12
2021
accepted:
27
06
2022
pubmed:
21
7
2022
medline:
21
9
2022
entrez:
20
7
2022
Statut:
ppublish
Résumé
Life expectancy in the UK has increased since the 19th century. As of 2019, there are just under 12 million people in the UK aged 65 or over, with close to a quarter living by themselves. Thus, many families and carers are looking for new ways to improve the health and care of older people. Passive sensors such as infra-red motion and plug sensors have had success as a noninvasive way to help the older people. These provide a series of categorical sensor events throughout the day. Modeling this categorical dataset can help to understand and predict behavior. This article proposes a method to model the probability a sensor will trigger throughout the day for a household whilst accounting for the prior data and other sensors within the home. We present our results on a dataset from Howz, a company helping people to passively identify changes in their behavior over time.
Identifiants
pubmed: 35858766
doi: 10.1002/sim.9529
pmc: PMC9796002
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4629-4646Informations de copyright
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Références
BMC Geriatr. 2018 Dec 5;18(1):269
pubmed: 30514225
Biostatistics. 2020 Oct 1;21(4):709-726
pubmed: 30753436
Proc Mach Learn Res. 2018 Aug;85:312-331
pubmed: 30899917
Stat Med. 2022 Oct 15;41(23):4629-4646
pubmed: 35858766