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Modeling and forecasting of at home activity in older adults using passive sensor technology

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Modeling and forecasting of at home activity in older adults using passive sensor technology. / Gillam, Jess; Killick, Rebecca; Heal, Jack et al.
In: Statistics in Medicine, Vol. 41, No. 23, 31.10.2022, p. 4629-4646.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gillam, J, Killick, R, Heal, J & Norwood, B 2022, 'Modeling and forecasting of at home activity in older adults using passive sensor technology', Statistics in Medicine, vol. 41, no. 23, pp. 4629-4646. https://doi.org/10.1002/sim.9529

APA

Gillam, J., Killick, R., Heal, J., & Norwood, B. (2022). Modeling and forecasting of at home activity in older adults using passive sensor technology. Statistics in Medicine, 41(23), 4629-4646. https://doi.org/10.1002/sim.9529

Vancouver

Gillam J, Killick R, Heal J, Norwood B. Modeling and forecasting of at home activity in older adults using passive sensor technology. Statistics in Medicine. 2022 Oct 31;41(23):4629-4646. Epub 2022 Jul 20. doi: 10.1002/sim.9529

Author

Gillam, Jess ; Killick, Rebecca ; Heal, Jack et al. / Modeling and forecasting of at home activity in older adults using passive sensor technology. In: Statistics in Medicine. 2022 ; Vol. 41, No. 23. pp. 4629-4646.

Bibtex

@article{38a70beff0984399af72e54e4257093a,
title = "Modeling and forecasting of at home activity in older adults using passive sensor technology",
abstract = "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.",
keywords = "autoregressive, binary series, home sensing",
author = "Jess Gillam and Rebecca Killick and Jack Heal and Ben Norwood",
year = "2022",
month = oct,
day = "31",
doi = "10.1002/sim.9529",
language = "English",
volume = "41",
pages = "4629--4646",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "23",

}

RIS

TY - JOUR

T1 - Modeling and forecasting of at home activity in older adults using passive sensor technology

AU - Gillam, Jess

AU - Killick, Rebecca

AU - Heal, Jack

AU - Norwood, Ben

PY - 2022/10/31

Y1 - 2022/10/31

N2 - 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.

AB - 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.

KW - autoregressive

KW - binary series

KW - home sensing

UR - http://www.scopus.com/inward/record.url?scp=85135060000&partnerID=8YFLogxK

U2 - 10.1002/sim.9529

DO - 10.1002/sim.9529

M3 - Journal article

VL - 41

SP - 4629

EP - 4646

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 23

ER -