Using semi-Markov models to identify long holding times of activities of daily living in smart homes

Lingkai Yang; Sally McClean; Abul Bashar; Samuel Moore; Zeeshan Tariq

doi:10.1109/swc57546.2023.10449129

Using semi-Markov models to identify long holding times of activities of daily living in smart homes

Lingkai Yang
, Sally McClean
, Abul Bashar
, Samuel Moore
, Zeeshan Tariq

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

4 Citations (Scopus)

Abstract

Monitoring the activities of daily living (ADL) in smart homes is crucial, especially for older people and patients, to maintain or enhance their functioning, independence, and overall well-being. Additionally, by detecting unusual or abnormal inhabitant behaviour, it provides an opportunity for facilitating reminders, customised assistance for ADL completion, or alarms to notify carers or medical services. This study utilizes semi-Markov models integrated with gamma mixture models for modelling ADLs, as well as identifying anomalies, especially long holding times. The method is evaluated on a publicly available sensorised smart home dataset, with 2, 12 and 2 anomalies detected in activities, sensors and locations respectively, demonstrating its effectiveness in ADL modelling and anomaly detection.

Original language	English
Title of host publication	Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	979-8-3503-1980-4
ISBN (Print)	979-8-3503-1981-1
DOIs	https://doi.org/10.1109/swc57546.2023.10449129
Publication status	Published (in print/issue) - 1 Mar 2024
Event	2023 IEEE Smart World Congress - Portsmouth, United Kingdom Duration: 28 Aug 2023 → 31 Aug 2023 Conference number: 2023

Publication series

Name	Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

Conference

Conference	2023 IEEE Smart World Congress
Abbreviated title	SWC
Country/Territory	United Kingdom
City	Portsmouth
Period	28/08/23 → 31/08/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

This research is supported by BTIIC (the British Telecom Ireland Innovation Centre), funded by British Telecom and Invest Northern Ireland

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Smart homes
Mixture models
Medical services
Monitoring
Intelligent sensors
Anomaly detection
Long holding times
Activities of daily living
Gamma mixture model
Semi-Markov model

Access to Document

10.1109/swc57546.2023.10449129

Cite this

Yang, L., McClean, S., Bashar, A., Moore, S., & Tariq, Z. (2024). Using semi-Markov models to identify long holding times of activities of daily living in smart homes. In Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023 (pp. 1-6). (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023). IEEE. https://doi.org/10.1109/swc57546.2023.10449129

Yang, Lingkai ; McClean, Sally ; Bashar, Abul et al. / Using semi-Markov models to identify long holding times of activities of daily living in smart homes. Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. IEEE, 2024. pp. 1-6 (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023).

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title = "Using semi-Markov models to identify long holding times of activities of daily living in smart homes",

abstract = "Monitoring the activities of daily living (ADL) in smart homes is crucial, especially for older people and patients, to maintain or enhance their functioning, independence, and overall well-being. Additionally, by detecting unusual or abnormal inhabitant behaviour, it provides an opportunity for facilitating reminders, customised assistance for ADL completion, or alarms to notify carers or medical services. This study utilizes semi-Markov models integrated with gamma mixture models for modelling ADLs, as well as identifying anomalies, especially long holding times. The method is evaluated on a publicly available sensorised smart home dataset, with 2, 12 and 2 anomalies detected in activities, sensors and locations respectively, demonstrating its effectiveness in ADL modelling and anomaly detection.",

keywords = "Smart homes, Mixture models, Medical services, Monitoring, Intelligent sensors, Anomaly detection, Long holding times, Activities of daily living, Gamma mixture model, Semi-Markov model",

author = "Lingkai Yang and Sally McClean and Abul Bashar and Samuel Moore and Zeeshan Tariq",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Smart World Congress, SWC ; Conference date: 28-08-2023 Through 31-08-2023",

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booktitle = "Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023",

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Yang, L, McClean, S, Bashar, A, Moore, S & Tariq, Z 2024, Using semi-Markov models to identify long holding times of activities of daily living in smart homes. in Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023, IEEE, pp. 1-6, 2023 IEEE Smart World Congress, Portsmouth, United Kingdom, 28/08/23. https://doi.org/10.1109/swc57546.2023.10449129

Using semi-Markov models to identify long holding times of activities of daily living in smart homes. / Yang, Lingkai; McClean, Sally; Bashar, Abul et al.
Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. IEEE, 2024. p. 1-6 (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023).

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

TY - GEN

T1 - Using semi-Markov models to identify long holding times of activities of daily living in smart homes

AU - Yang, Lingkai

AU - McClean, Sally

AU - Bashar, Abul

AU - Moore, Samuel

AU - Tariq, Zeeshan

N1 - Conference code: 2023

PY - 2024/3/1

Y1 - 2024/3/1

N2 - Monitoring the activities of daily living (ADL) in smart homes is crucial, especially for older people and patients, to maintain or enhance their functioning, independence, and overall well-being. Additionally, by detecting unusual or abnormal inhabitant behaviour, it provides an opportunity for facilitating reminders, customised assistance for ADL completion, or alarms to notify carers or medical services. This study utilizes semi-Markov models integrated with gamma mixture models for modelling ADLs, as well as identifying anomalies, especially long holding times. The method is evaluated on a publicly available sensorised smart home dataset, with 2, 12 and 2 anomalies detected in activities, sensors and locations respectively, demonstrating its effectiveness in ADL modelling and anomaly detection.

AB - Monitoring the activities of daily living (ADL) in smart homes is crucial, especially for older people and patients, to maintain or enhance their functioning, independence, and overall well-being. Additionally, by detecting unusual or abnormal inhabitant behaviour, it provides an opportunity for facilitating reminders, customised assistance for ADL completion, or alarms to notify carers or medical services. This study utilizes semi-Markov models integrated with gamma mixture models for modelling ADLs, as well as identifying anomalies, especially long holding times. The method is evaluated on a publicly available sensorised smart home dataset, with 2, 12 and 2 anomalies detected in activities, sensors and locations respectively, demonstrating its effectiveness in ADL modelling and anomaly detection.

KW - Smart homes

KW - Mixture models

KW - Medical services

KW - Monitoring

KW - Intelligent sensors

KW - Anomaly detection

KW - Long holding times

KW - Activities of daily living

KW - Gamma mixture model

KW - Semi-Markov model

UR - https://www.scopus.com/pages/publications/85187402198

U2 - 10.1109/swc57546.2023.10449129

DO - 10.1109/swc57546.2023.10449129

M3 - Conference contribution

SN - 979-8-3503-1981-1

T3 - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

SP - 1

EP - 6

BT - Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023

PB - IEEE

T2 - 2023 IEEE Smart World Congress

Y2 - 28 August 2023 through 31 August 2023

ER -

Yang L, McClean S, Bashar A, Moore S , Tariq Z. Using semi-Markov models to identify long holding times of activities of daily living in smart homes. In Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023. IEEE. 2024. p. 1-6. (Proceedings - 2023 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Autonomous and Trusted Vehicles, Scalable Computing and Communications, Digital Twin, Privacy Computing and Data Security, Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PCDS/Metaverse 2023). Epub 2024 Mar 1. doi: 10.1109/swc57546.2023.10449129

Using semi-Markov models to identify long holding times of activities of daily living in smart homes

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Improving reliability in the internet of things through anomaly detection

Cite this

Using semi-Markov models to identify long holding times of activities of daily living in smart homes

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Improving reliability in the internet of things through anomaly detection

Cite this