A CSI-Based Human Activity Recognition Using Deep Learning

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cos...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-10, Vol.21 (21), p.7225
Main Authors: Fard Moshiri, Parisa, Shahbazian, Reza, Nabati, Mohammad, Ghorashi, Seyed Ali
Format: Article
Language:English
Subjects:
activity recognition
Algorithms
channel state information
Datasets
Deep learning
Human activity recognition
Internet of Things
Methods
Moving object recognition
Neural networks
Older people
Privacy
Sensors
smart house
Smart houses
Software
Wireless access points
Wireless networks
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Summary:The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21217225