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. 2019 Feb 13;19(4):766.
doi: 10.3390/s19040766.

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Hemant Ghayvat  1 , Muhammad Awais  2 , Sharnil Pandya  3 , Hao Ren  4 , Saeed Akbarzadeh  5 , Subhas Chandra Mukhopadhyay  6 , Chen Chen  7 , Prosanta Gope  8 , Arpita Chouhan  9 , Wei Chen  10
Affiliations

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection

Hemant Ghayvat et al. Sensors (Basel). .

Abstract

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).

Keywords: activity of daily living; ambient assisted living; anomaly detection; elderly; smart home; wellness; wellness indices.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The bottom-up diagram to represent the Smart Home-based AAL system.
Figure 2
Figure 2
A more than seven-decade-old house where the Smart Aging system was installed without any significant changes in the house.
Figure 3
Figure 3
The layout of heterogeneous sensing units’ deployment in the AAL.
Figure 4
Figure 4
(a) Outdoor temperature sensing unit. (b) Activity-based manual indication unit. (c) Movement sensor deployed at the entry. (d) Movement sensor deployed at the corner of the washbasin in kitchen. (e) Water cattle plugged into E & E Sensor. (f) Rice cooker plugged into E & E Sensor. (g) Microwave oven plugged into E & E Sensor. (h) Television plugged into E & E Sensor.
Figure 5
Figure 5
(a) Contact sensing to know about the usage of the shower. (b) Force sensor deployed over the toilet seat. (c) Local Home Gateway Server.
Figure 6
Figure 6
Activities of daily living.
Figure 7
Figure 7
Modeling sub-activity for Activities of Daily Living.
Figure 8
Figure 8
(a) The (Basic) Ϣ1 for four different houses up to one week. (b) The (Basic) Ϣ2 for sleeping activity for four different houses up to one week.
Figure 9
Figure 9
(a) The activities during the course of the day. (b) The activities of taking medication and food during the course of the day.
See this image and copyright information in PMC

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