Published on Tue Apr 23 2019

HAUAR: Home Automation Using Action Recognition

Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu, Venkanna Udutalapally

The accuracy of the system was 90% in the real-life test experiments. We recognize the three actions of a person (sitting, standing andlying) along with the recognition of an empty room.

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Abstract

Today, many of the home automation systems deployed are mostly controlled by humans. This control by humans restricts the automation of home appliances to an extent. Also, most of the deployed home automation systems use the Internet of Things technology to control the appliances. In this paper, we propose a system developed using action recognition to fully automate the home appliances. We recognize the three actions of a person (sitting, standing and lying) along with the recognition of an empty room. The accuracy of the system was 90% in the real-life test experiments. With this system, we remove the human intervention in home automation systems for controlling the home appliances and at the same time we ensure the data privacy and reduce the energy consumption by efficiently and optimally using home appliances.

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Action monitoring in a home environment provides important information for health monitoring. The sensor is not only different by being thermal, but it is also of low resolution: 8x8 pixels. The combination of the thermal imaging and low spatial resolution ensures the privacy of individuals.
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This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior. The framework consists of two novel functionalities. It consists of an intelligent decision-making algorithm that can analyze these behavioral patterns.
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AdaSense is a sensing, feature extraction and classification co-optimized framework for Human Activity Recognition. The proposed techniques reduce the power consumption by switching among different sensor configurations as a function of user activity.
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