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Saeed Akbarzadeh
CIME, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.

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Journal article
Published: 13 February 2019 in Sensors
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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).

ACS Style

Hemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors 2019, 19, 766 .

AMA Style

Hemant Ghayvat, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan, Wei Chen. Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection. Sensors. 2019; 19 (4):766.

Chicago/Turabian Style

Hemant Ghayvat; Muhammad Awais; Sharnil Pandya; Hao Ren; Saeed Akbarzadeh; Subhas Chandra Mukhopadhyay; Chen Chen; Prosanta Gope; Arpita Chouhan; Wei Chen. 2019. "Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection." Sensors 19, no. 4: 766.

Journal article
Published: 23 October 2018 in Applied System Innovation
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The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.

ACS Style

Sharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation 2018, 1, 42 .

AMA Style

Sharnil Pandya, Hemant Ghayvat, Ketan Kotecha, Mohammed Awais, Saeed Akbarzadeh, Prosanta Gope, Subhas Chandra Mukhopadhyay, Wei Chen. Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol. Applied System Innovation. 2018; 1 (4):42.

Chicago/Turabian Style

Sharnil Pandya; Hemant Ghayvat; Ketan Kotecha; Mohammed Awais; Saeed Akbarzadeh; Prosanta Gope; Subhas Chandra Mukhopadhyay; Wei Chen. 2018. "Smart Home Anti-Theft System: A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol." Applied System Innovation 1, no. 4: 42.