Smart aging system: uncovering the hidden wellness parameter for well-being monitoring and anomaly detection

Hemant Ghayvat, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan, Wei Chen

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

LanguageEnglish
Article number766
Number of pages32
JournalSensors (Switzerland)
Volume19
Issue number4
DOIs
Publication statusPublished - 2 Feb 2019

Fingerprint

Activities of Daily Living
Sensor data fusion
Aging of materials
anomalies
Life Style
Monitoring
multisensor fusion
Weather
Interpersonal Relations
Sample Size
Caregivers
Names
sensors
Chemical activation
Health
Learning
Physicians
Efficiency
Sensitivity and Specificity
Temperature

Bibliographical note

Copyright the Author(s). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • wellness
  • elderly
  • smart home
  • ambient assisted living
  • activity of daily living
  • wellness indices
  • anomaly detection

Cite this

Ghayvat, Hemant ; Awais, Muhammad ; Pandya, Sharnil ; Ren, Hao ; Akbarzadeh, Saeed ; Mukhopadhyay, Subhas Chandra ; Chen, Chen ; Gope, Prosanta ; Chouhan, Arpita ; Chen, Wei. / Smart aging system : uncovering the hidden wellness parameter for well-being monitoring and anomaly detection. In: Sensors (Switzerland). 2019 ; Vol. 19, No. 4.
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title = "Smart aging system: uncovering the hidden wellness parameter for well-being monitoring and anomaly detection",
abstract = "Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17{\%} ± 0.95) from (80.81{\%} ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).",
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note = "Copyright the Author(s). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",
year = "2019",
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Ghayvat, H, Awais, M, Pandya, S, Ren, H, Akbarzadeh, S, Mukhopadhyay, SC, Chen, C, Gope, P, Chouhan, A & Chen, W 2019, 'Smart aging system: uncovering the hidden wellness parameter for well-being monitoring and anomaly detection', Sensors (Switzerland), vol. 19, no. 4, 766. https://doi.org/10.3390/s19040766

Smart aging system : uncovering the hidden wellness parameter for well-being monitoring and anomaly detection. / Ghayvat, Hemant; Awais, Muhammad; Pandya, Sharnil; Ren, Hao; Akbarzadeh, Saeed; Mukhopadhyay, Subhas Chandra; Chen, Chen; Gope, Prosanta; Chouhan, Arpita; Chen, Wei.

In: Sensors (Switzerland), Vol. 19, No. 4, 766, 02.02.2019.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Smart aging system

T2 - Sensors

AU - Ghayvat, Hemant

AU - Awais, Muhammad

AU - Pandya, Sharnil

AU - Ren, Hao

AU - Akbarzadeh, Saeed

AU - Mukhopadhyay, Subhas Chandra

AU - Chen, Chen

AU - Gope, Prosanta

AU - Chouhan, Arpita

AU - Chen, Wei

N1 - Copyright the Author(s). Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2019/2/2

Y1 - 2019/2/2

N2 - Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

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