ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome

Yu Chen; Tom Diethe; Peter Flach

ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome

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Abstract

We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.

Original language	English
Title of host publication	Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Editors	Subbarao Kambhampati
Publisher	AAAI Press
Pages	1404-1410
Number of pages	7
ISBN (Print)	978-1-57735-770-4
Publication status	Published - 15 Jul 2016

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Jean Golding
SPHERE

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SPHERE (EPSRC IRC)
Craddock, I. J. (Principal Investigator), Coyle, D. T. (Principal Investigator), Flach, P. A. (Principal Investigator), Kaleshi, D. (Principal Investigator), Mirmehdi, M. (Principal Investigator), Piechocki, R. J. (Principal Investigator), Stark, B. H. (Principal Investigator), Ascione, R. (Co-Principal Investigator), Ashburn, A. M. (Collaborator), Burnett, M. E. (Collaborator), Damen, D. (Co-Principal Investigator), Gooberman-Hill, R. (Principal Investigator), Harwin, W. S. (Collaborator), Hilton, G. (Co-Principal Investigator), Holderbaum, W. (Collaborator), Holley, A. P. (Manager), Manchester, V. A. (Administrator), Meller, B. J. (Other ), Stack, E. (Collaborator) & Gilchrist, I. D. (Principal Investigator)
1/10/13 → 30/09/18
Project: Research, Parent

Efficient Continual Learning: Approaches and Measures
Chen, Y. (Author), Flach, P. (Supervisor) & Diethe, T. (Supervisor), 28 Sept 2021
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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@inproceedings{33b3437afc9a45ae8afe07c03c4aeaa3,

title = "ADL{\texttrademark}: A Topic Model for Recognition of Activities of Daily Living in a SmartHome",

abstract = "We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.",

author = "Yu Chen and Tom Diethe and Peter Flach",

year = "2016",

month = jul,

day = "15",

language = "English",

isbn = "978-1-57735-770-4",

pages = "1404--1410",

editor = "Subbarao Kambhampati",

booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence",

publisher = "AAAI Press",

address = "United States",

}

ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome. / Chen, Yu; Diethe, Tom; Flach, Peter.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. ed. / Subbarao Kambhampati. AAAI Press, 2016. p. 1404-1410.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - ADL™

T2 - A Topic Model for Recognition of Activities of Daily Living in a SmartHome

AU - Chen, Yu

AU - Diethe, Tom

AU - Flach, Peter

PY - 2016/7/15

Y1 - 2016/7/15

N2 - We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.

AB - We present an unsupervised approach for discovery of Activities of Daily Living (ADL) in a smart home. Activity discovery is an important enabling technology, for example to tackle the healthcare requirements of elderly people in their homes. The technique applied most often is supervised learning, which relies on expensive labelled data and lacks the flexibility to discover unseen activities. Building on ideas from text mining, we present a powerful topic model and a segmentation algorithm that can learn from unlabelled sensor data. The model has been evaluated extensively on datasets collected from real smart homes. The results demonstrate that this approach can successfully discover the activities of residents, and can be effectively used in a range of applications such as detection of abnormal activities and monitoring of sleep quality, among many others.

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-57735-770-4

SP - 1404

EP - 1410

BT - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence

A2 - Kambhampati, Subbarao

PB - AAAI Press

ER -

ADL™: A Topic Model for Recognition of Activities of Daily Living in a SmartHome

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SPHERE (EPSRC IRC)

Student theses

Efficient Continual Learning: Approaches and Measures

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