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

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

10 Citations (Scopus)

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
Pages	219–235
ISBN (Electronic)	978-3-319-26005-1
DOIs	https://doi.org/10.1007/978-3-319-26005-1_15
Publication status	Published - 30 Oct 2015
Externally published	Yes
Event	12th European Conference on Ambient Intelligence, AMI 2015 - Athens, Greece Duration: 11 Nov 2015 → 15 Nov 2015 Conference number: 12

Conference

Conference	12th European Conference on Ambient Intelligence, AMI 2015
Abbreviated title	AMI 2015
Country/Territory	Greece
City	Athens
Period	11/11/15 → 15/11/15

Keywords

n/a OA procedure

Access to Document

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

Cite this

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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.",

keywords = "n/a OA procedure",

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

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

language = "English",

isbn = "978-3-319-26004-4",

pages = "219–235",

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note = "12th European Conference on Ambient Intelligence, AMI 2015, AMI 2015 ; Conference date: 11-11-2015 Through 15-11-2015",

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Nait Aicha, A, Englebienne, G & Kröse, B 2015, Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home. in Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings. pp. 219–235, 12th European Conference on Ambient Intelligence, AMI 2015, 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. 2015. p. 219–235.

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

TY - GEN

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

AU - Nait Aicha, Ahmed

AU - Englebienne, Gwenn

AU - Kröse, Ben

N1 - Conference code: 12

PY - 2015/10/30

Y1 - 2015/10/30

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.

KW - n/a OA procedure

U2 - 10.1007/978-3-319-26005-1_15

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

M3 - Conference contribution

SN - 978-3-319-26004-4

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EP - 235

BT - Ambient Intelligence

T2 - 12th European Conference on Ambient Intelligence, AMI 2015

Y2 - 11 November 2015 through 15 November 2015

ER -

Continuous Gait Velocity Analysis Using Ambient Sensors in a Smart Home

Abstract

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Keywords

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