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

Sensors

卷:19

Hao Ren¹  Hemant Ghayvat²  Muhammad Awais²  Subhas Chandra Mukhopadhyay²  Saeed Akbarzadeh²  Wei Chen²  Prosanta Gope²  Sharnil Pandya³  Chen Chen⁴  Arpita Chouhan⁵ 

[1] Engineering Department, Navrachana University, Vadodara, Gujarat 391410, India;

[2] CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China;

[3] Computer Science &

[4] Mechanical/Electronics Engineering, Macquarie University, Sydney NSW 2109, Australia;

[5] School of Engineering and Computer Science, University of Hull, Hull HU1 1DB, UK;

关键词: wellness;    elderly;    smart home;    ambient assisted living;    activity of daily living;    wellness indices;    anomaly detection;   

DOI  :  10.3390/s19040766

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown