How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data

Ahmed Nait Aicha; Gwenn Englebienne; Ben Kröse

doi:10.1145/2494091.2497283

How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data

Ahmed Nait Aicha
, Gwenn Englebienne
, Ben Kröse

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

21 Citations (Scopus)

Abstract

Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In reality, the resident receives visits from family members or professional health care givers. In such cases activity recognition must take into account the presence of multiple persons. Here we investigate the problem of detecting multiple persons in a home environment equipped with a sensor network consisting of 13 binary sensors. We collected data during more than one year in our living labs and used Hidden Markov Model (HMM) for a visitor detection. A cross validation method was used to determine the best set of features from the binary data. Using this set of features the detection rate is approximately 85%.

Original language	English
Title of host publication	UbiComp 2013 Adjunct
Subtitle of host publication	Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing
Editors	Friedemann Mattern
Publisher	ACM Publishing
Pages	1285-1294
Number of pages	10
ISBN (Print)	978-1-4503-2215-7
DOIs	https://doi.org/10.1145/2494091.2497283
Publication status	Published - 2013
Externally published	Yes
Event	ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013 - Zurich, Switzerland Duration: 8 Sept 2013 → 12 Sept 2013

Workshop

Workshop	ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013
Abbreviated title	UbiComp 2013
Country/Territory	Switzerland
City	Zurich
Period	8/09/13 → 12/09/13

Keywords

Ambient assisted living and health monitoring
Hidden markov models
Sensor networks for pervasive health care
n/a OA procedure

Access to Document

10.1145/2494091.2497283

Cite this

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title = "How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data",

abstract = "Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In reality, the resident receives visits from family members or professional health care givers. In such cases activity recognition must take into account the presence of multiple persons. Here we investigate the problem of detecting multiple persons in a home environment equipped with a sensor network consisting of 13 binary sensors. We collected data during more than one year in our living labs and used Hidden Markov Model (HMM) for a visitor detection. A cross validation method was used to determine the best set of features from the binary data. Using this set of features the detection rate is approximately 85\%.",

keywords = "Ambient assisted living and health monitoring, Hidden markov models, Sensor networks for pervasive health care, n/a OA procedure",

author = "\{Nait Aicha\}, Ahmed and Gwenn Englebienne and Ben Kr{\"o}se",

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doi = "10.1145/2494091.2497283",

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pages = "1285--1294",

editor = "Friedemann Mattern",

booktitle = "UbiComp 2013 Adjunct",

publisher = "ACM Publishing",

note = "ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013, UbiComp 2013 ; Conference date: 08-09-2013 Through 12-09-2013",

}

Nait Aicha, A, Englebienne, G & Kröse, B 2013, How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data. in F Mattern (ed.), UbiComp 2013 Adjunct: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing. ACM Publishing, pp. 1285-1294, ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2013, Zurich, Switzerland, 8/09/13. https://doi.org/10.1145/2494091.2497283

How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data. / Nait Aicha, Ahmed; Englebienne, Gwenn; Kröse, Ben.
UbiComp 2013 Adjunct: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing. ed. / Friedemann Mattern. ACM Publishing, 2013. p. 1285-1294.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data

AU - Nait Aicha, Ahmed

AU - Englebienne, Gwenn

AU - Kröse, Ben

PY - 2013

Y1 - 2013

N2 - Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In reality, the resident receives visits from family members or professional health care givers. In such cases activity recognition must take into account the presence of multiple persons. Here we investigate the problem of detecting multiple persons in a home environment equipped with a sensor network consisting of 13 binary sensors. We collected data during more than one year in our living labs and used Hidden Markov Model (HMM) for a visitor detection. A cross validation method was used to determine the best set of features from the binary data. Using this set of features the detection rate is approximately 85%.

AB - Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In reality, the resident receives visits from family members or professional health care givers. In such cases activity recognition must take into account the presence of multiple persons. Here we investigate the problem of detecting multiple persons in a home environment equipped with a sensor network consisting of 13 binary sensors. We collected data during more than one year in our living labs and used Hidden Markov Model (HMM) for a visitor detection. A cross validation method was used to determine the best set of features from the binary data. Using this set of features the detection rate is approximately 85%.

KW - Ambient assisted living and health monitoring

KW - Hidden markov models

KW - Sensor networks for pervasive health care

KW - n/a OA procedure

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DO - 10.1145/2494091.2497283

M3 - Conference contribution

AN - SCOPUS:84885195698

SN - 978-1-4503-2215-7

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BT - UbiComp 2013 Adjunct

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Y2 - 8 September 2013 through 12 September 2013

ER -

How lonely is your grandma? detecting the visits to assisted living elderly from wireless sensor network data

Abstract

Workshop

Keywords

Access to Document

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