Authors :
Aishwarya S; Apeksha S; Shivani N S; Shivani N S; Sirisha J C
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
DOI :
https://doi.org/10.5281/zenodo.6972696
Abstract :
The use of ‘Internet of Things’ in homes has
grown rapidly over the past few years. However, these
systems have always been operated via a remote control,
voice control or through user applications. In this paper,
we are proposing a system design that provides a way for
identifying a wide range of human emotions by employing
a hybrid deep learning CNN algorithm to index the
emotional state and send the same over a ZigBee wireless
module to an Arduino. In order to reflect the user's
sentiment, the music and lighting are altered by the
Arduino. The system is also equipped with an LCD device
that displays the current emotion and room temperature
as measured by the temperature sensor.
Keywords :
Home Automation, Neural Networks, CNN-LSTM, Facial expression recognition, IoT, Arduino, Zigbee
The use of ‘Internet of Things’ in homes has
grown rapidly over the past few years. However, these
systems have always been operated via a remote control,
voice control or through user applications. In this paper,
we are proposing a system design that provides a way for
identifying a wide range of human emotions by employing
a hybrid deep learning CNN algorithm to index the
emotional state and send the same over a ZigBee wireless
module to an Arduino. In order to reflect the user's
sentiment, the music and lighting are altered by the
Arduino. The system is also equipped with an LCD device
that displays the current emotion and room temperature
as measured by the temperature sensor.
Keywords :
Home Automation, Neural Networks, CNN-LSTM, Facial expression recognition, IoT, Arduino, Zigbee