Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

Ahmed Nait Aicha; Gwenn Englebienne; Ben Kröse

doi:10.1007/978-3-319-26005-1_15

Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

Ahmed Nait Aicha, Gwenn Englebienne, Ben Kröse

Digital life

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

9 Citations (Scopus)

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Abstract

We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments.

Original language	English
Title of host publication	Ambient Intelligence
Subtitle of host publication	12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings
Editors	Boris De Ruyter, Achilles Kameas, Periklis Chatzimisios, Irene Mavrommati
Place of Publication	Cham
Publisher	Springer
Pages	219-235
ISBN (Electronic)	9783319260051
ISBN (Print)	9783319260044
DOIs	https://doi.org/10.1007/978-3-319-26005-1_15
Publication status	Published - 2015
Event	Ambient Intelligence: 12th European Conference - Athens, Greece Duration: 11 Nov 2015 → 13 Nov 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9425

Conference

Conference	Ambient Intelligence
Abbreviated title	AML 2015
Country/Territory	Greece
City	Athens
Period	11/11/15 → 13/11/15

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10.1007/978-3-319-26005-1_15

Continuous gait velocityAuthor accepted manuscript (AAM), 572 KB

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Nait Aicha, A., Englebienne, G., & Kröse, B. (2015). Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. In B. De Ruyter, A. Kameas, P. Chatzimisios, & I. Mavrommati (Eds.), Ambient Intelligence: 12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings (pp. 219-235). (Lecture Notes in Computer Science; Vol. 9425). Springer. https://doi.org/10.1007/978-3-319-26005-1_15

Nait Aicha, Ahmed ; Englebienne, Gwenn ; Kröse, Ben. / Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. Ambient Intelligence: 12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings. editor / Boris De Ruyter ; Achilles Kameas ; Periklis Chatzimisios ; Irene Mavrommati. Cham : Springer, 2015. pp. 219-235 (Lecture Notes in Computer Science).

@inproceedings{abe09193250347d79a341c0e41fd3c0b,

title = "Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home",

abstract = "We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments.",

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

year = "2015",

doi = "10.1007/978-3-319-26005-1_15",

language = "English",

isbn = "9783319260044",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "219--235",

editor = "{De Ruyter}, {Boris } and Achilles Kameas and { Chatzimisios}, Periklis and { Mavrommati}, Irene",

booktitle = "Ambient Intelligence",

note = "Ambient Intelligence : 12th European Conference, AML 2015 ; Conference date: 11-11-2015 Through 13-11-2015",

}

Nait Aicha, A, Englebienne, G & Kröse, B 2015, Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. in B De Ruyter, A Kameas, P Chatzimisios & I Mavrommati (eds), Ambient Intelligence: 12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings. Lecture Notes in Computer Science, vol. 9425, Springer, Cham, pp. 219-235, Ambient Intelligence, Athens, Greece, 11/11/15. https://doi.org/10.1007/978-3-319-26005-1_15

Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. / Nait Aicha, Ahmed; Englebienne, Gwenn; Kröse, Ben.
Ambient Intelligence: 12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings. ed. / Boris De Ruyter; Achilles Kameas; Periklis Chatzimisios; Irene Mavrommati. Cham: Springer, 2015. p. 219-235 (Lecture Notes in Computer Science; Vol. 9425).

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

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T1 - Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

AU - Nait Aicha, Ahmed

AU - Englebienne, Gwenn

AU - Kröse, Ben

PY - 2015

Y1 - 2015

N2 - We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments.

AB - We present a method for measuring gait velocity using data from an existing ambient sensor network. Gait velocity is an important predictor of fall risk and functional health. In contrast to other approaches that use specific sensors or sensor configurations our method imposes no constraints on the elderly. We studied different probabilistic models for the description of the sensor patterns. Experiments are carried out on 15 months of data and include repeated assessments from an occupational therapist. We showed that the measured gait velocities correlate with these assessments.

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DO - 10.1007/978-3-319-26005-1_15

M3 - Conference contribution

SN - 9783319260044

T3 - Lecture Notes in Computer Science

SP - 219

EP - 235

BT - Ambient Intelligence

A2 - De Ruyter, Boris

A2 - Kameas, Achilles

A2 - Chatzimisios, Periklis

A2 - Mavrommati, Irene

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Nait Aicha A, Englebienne G, Kröse B. Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. In De Ruyter B, Kameas A, Chatzimisios P, Mavrommati I, editors, Ambient Intelligence: 12th European Conference, AmI 2015 Athens, Greece, November 11–13, 2015 proceedings. Cham: Springer. 2015. p. 219-235. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-26005-1_15

Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

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