The integration of the Internet of Things (IoT) and artificial
intelligence (AI) in healthcare is transforming patient care by enabling
real-time monitoring and personalized treatment. Facial expression
recognition (FER) plays a vital role in identifying emotional states,
which can improve mental health diagnosis and patient engagement.
However, existing FER systems face limitations in accuracy and
responsiveness due to insufficient data processing capabilities and lack
of adaptive learning models. This study proposes an IoT-driven FER
system using a Convolutional Neural Network with Attention
Mechanism (CNN-AM) to enhance emotion detection accuracy and
system adaptability. IoT devices, including wearable sensors and smart
cameras, capture real-time facial data, which is processed using the
CNN-AM model to identify emotional states. The attention mechanism
improves the model's ability to focus on critical facial features,
reducing false detection rates. The system was tested on the FER-2013
dataset, achieving a recognition accuracy of 95.6%, outperforming
existing methods such as Support Vector Machine (SVM) and ResNet
by 3.2% and 2.1%, respectively. Results demonstrate that the proposed
model enhances both detection speed and accuracy, offering a scalable
and efficient solution for personalized healthcare in Industry 5.0.
Varghese S. Chooralil1, Niby Babu2
Rajagiri School of Engineering and Technology, India1, CVV Institute of Science and Technology, Chinmaya Vishwa Vidyapeeth, India2
IoT, Facial Expression Recognition, Attention Mechanism, Personalized Healthcare, Industry 5.0
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 6 , Issue: 2 , Pages: 756 - 760 )
Date of Publication :
March 2025
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20
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