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Identifying irregular activity sequences: an application to passive household monitoring

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

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Identifying irregular activity sequences: an application to passive household monitoring. / Gillam, Jess; Killick, Rebecca; Taylor, Simon et al.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 72, No. 3, 30.06.2023, p. 519-543.

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

Harvard

Gillam, J, Killick, R, Taylor, S, Heal, J & Norwood, B 2023, 'Identifying irregular activity sequences: an application to passive household monitoring', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 72, no. 3, pp. 519-543. https://doi.org/10.1093/jrsssc/qlad005

APA

Gillam, J., Killick, R., Taylor, S., Heal, J., & Norwood, B. (2023). Identifying irregular activity sequences: an application to passive household monitoring. Journal of the Royal Statistical Society: Series C (Applied Statistics), 72(3), 519-543. https://doi.org/10.1093/jrsssc/qlad005

Vancouver

Gillam J, Killick R, Taylor S, Heal J, Norwood B. Identifying irregular activity sequences: an application to passive household monitoring. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2023 Jun 30;72(3):519-543. Epub 2023 May 17. doi: 10.1093/jrsssc/qlad005

Author

Gillam, Jess ; Killick, Rebecca ; Taylor, Simon et al. / Identifying irregular activity sequences : an application to passive household monitoring. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2023 ; Vol. 72, No. 3. pp. 519-543.

Bibtex

@article{b09b137aaa044ec3a867100c4030565f,
title = "Identifying irregular activity sequences: an application to passive household monitoring",
abstract = "Approximately one in five people will live to see their 100th birthday due to advancements in modern medicine and other factors. Over 65{\textquoteright}s constitute 42% of elective admissions and 43% of emergency admissions to hospitals. Increasingly, people are turning to technology to help improve health and care of the elderly. There is mixed evidence of the success of wearables in older populations with a key barrier being adoption. In contrast, passive sensors such as infra-red motion and plug sensors have had more success. These passive sensors give us a sequence of categorical “trigger” events throughout the day. This paper proposes a method for detecting subtle changes in sequences while taking account of the natural day-to-day variability and differing numbers of “trigger” events per day.",
keywords = "Statistics, Probability and Uncertainty, Statistics and Probability, Categorical data, Home sensing, Routines",
author = "Jess Gillam and Rebecca Killick and Simon Taylor and Jack Heal and Ben Norwood",
year = "2023",
month = jun,
day = "30",
doi = "10.1093/jrsssc/qlad005",
language = "English",
volume = "72",
pages = "519--543",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Identifying irregular activity sequences

T2 - an application to passive household monitoring

AU - Gillam, Jess

AU - Killick, Rebecca

AU - Taylor, Simon

AU - Heal, Jack

AU - Norwood, Ben

PY - 2023/6/30

Y1 - 2023/6/30

N2 - Approximately one in five people will live to see their 100th birthday due to advancements in modern medicine and other factors. Over 65’s constitute 42% of elective admissions and 43% of emergency admissions to hospitals. Increasingly, people are turning to technology to help improve health and care of the elderly. There is mixed evidence of the success of wearables in older populations with a key barrier being adoption. In contrast, passive sensors such as infra-red motion and plug sensors have had more success. These passive sensors give us a sequence of categorical “trigger” events throughout the day. This paper proposes a method for detecting subtle changes in sequences while taking account of the natural day-to-day variability and differing numbers of “trigger” events per day.

AB - Approximately one in five people will live to see their 100th birthday due to advancements in modern medicine and other factors. Over 65’s constitute 42% of elective admissions and 43% of emergency admissions to hospitals. Increasingly, people are turning to technology to help improve health and care of the elderly. There is mixed evidence of the success of wearables in older populations with a key barrier being adoption. In contrast, passive sensors such as infra-red motion and plug sensors have had more success. These passive sensors give us a sequence of categorical “trigger” events throughout the day. This paper proposes a method for detecting subtle changes in sequences while taking account of the natural day-to-day variability and differing numbers of “trigger” events per day.

KW - Statistics, Probability and Uncertainty

KW - Statistics and Probability

KW - Categorical data

KW - Home sensing

KW - Routines

U2 - 10.1093/jrsssc/qlad005

DO - 10.1093/jrsssc/qlad005

M3 - Journal article

VL - 72

SP - 519

EP - 543

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 3

ER -