Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

Matias Garcia-Constantino; Alexandros Konios; Mustafa Mustafa; Chris Nugent; Gareth Morrison

doi:10.1109/PerComWorkshops48775.2020.9156249

Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

Matias Garcia-Constantino, Alexandros Konios, Mustafa Mustafa, Chris Nugent, Gareth Morrison

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

15 Citations (Scopus)

Abstract

Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.

Original language	English
Title of host publication	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Publisher	IEEE Xplore
Number of pages	6
ISBN (Electronic)	978-1-7281-4716-1
ISBN (Print)	978-1-7281-4717-8
DOIs	https://doi.org/10.1109/PerComWorkshops48775.2020.9156249
Publication status	Published (in print/issue) - 4 Aug 2020
Event	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 - Austin, Austin, United States Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 18

Publication series

Name	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Conference

Conference	2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Abbreviated title	PerCom2020
Country/Territory	United States
City	Austin
Period	23/03/20 → 27/03/20

Keywords

Activities of Daily Living
ADLs
Sensor Fusion
Data Fusion
Activity Recognition

UN SDGs

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

Access to Document

10.1109/PerComWorkshops48775.2020.9156249Licence: Unspecified

Cite this

Garcia-Constantino, M., Konios, A., Mustafa, M., Nugent, C., & Morrison, G. (2020). Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 Article 9156249 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). IEEE Xplore. https://doi.org/10.1109/PerComWorkshops48775.2020.9156249

Garcia-Constantino, Matias ; Konios, Alexandros ; Mustafa, Mustafa et al. / Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. IEEE Xplore, 2020. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

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title = "Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living",

abstract = "Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.",

keywords = "Activities of Daily Living, ADLs, Sensor Fusion, Data Fusion, Activity Recognition",

author = "Matias Garcia-Constantino and Alexandros Konios and Mustafa Mustafa and Chris Nugent and Gareth Morrison",

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doi = "10.1109/PerComWorkshops48775.2020.9156249",

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Garcia-Constantino, M, Konios, A, Mustafa, M, Nugent, C & Morrison, G 2020, Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. in 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020., 9156249, 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, IEEE Xplore, 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020, Austin, Texas, United States, 23/03/20. https://doi.org/10.1109/PerComWorkshops48775.2020.9156249

Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. / Garcia-Constantino, Matias; Konios, Alexandros; Mustafa, Mustafa et al.
2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. IEEE Xplore, 2020. 9156249 (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020).

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

TY - GEN

T1 - Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

AU - Garcia-Constantino, Matias

AU - Konios, Alexandros

AU - Mustafa, Mustafa

AU - Nugent, Chris

AU - Morrison, Gareth

N1 - Conference code: 18

PY - 2020/8/4

Y1 - 2020/8/4

N2 - Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.

AB - Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.

KW - Activities of Daily Living

KW - ADLs

KW - Sensor Fusion

KW - Data Fusion

KW - Activity Recognition

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U2 - 10.1109/PerComWorkshops48775.2020.9156249

DO - 10.1109/PerComWorkshops48775.2020.9156249

M3 - Conference contribution

SN - 978-1-7281-4717-8

T3 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

BT - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

PB - IEEE Xplore

T2 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Y2 - 23 March 2020 through 27 March 2020

ER -

Garcia-Constantino M, Konios A, Mustafa M, Nugent C, Morrison G. Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020. IEEE Xplore. 2020. 9156249. (2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020). doi: 10.1109/PerComWorkshops48775.2020.9156249

Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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