  Ambient computing is gaining popularity as a major technological advancement
for the future. The modern era has witnessed a surge in the advancement in
healthcare systems, with viable radio frequency solutions proposed for remote
and unobtrusive human activity recognition (HAR). Specifically, this study
investigates the use of Wi-Fi channel state information (CSI) as a novel method
of ambient sensing that can be employed as a contactless means of recognizing
human activity in indoor environments. These methods avoid additional costly
hardware required for vision-based systems, which are privacy-intrusive, by
(re)using Wi-Fi CSI for various safety and security applications. During an
experiment utilizing universal software-defined radio (USRP) to collect CSI
samples, it was observed that a subject engaged in six distinct activities,
which included no activity, standing, sitting, and leaning forward, across
different areas of the room. Additionally, more CSI samples were collected when
the subject walked in two different directions. This study presents a Wi-Fi
CSI-based HAR system that assesses and contrasts deep learning approaches,
namely convolutional neural network (CNN), long short-term memory (LSTM), and
hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental
results indicate that LSTM surpasses current models and achieves an average
accuracy of 95.3% in multi-activity classification when compared to CNN and
hybrid techniques. In the future, research needs to study the significance of
resilience in diverse and dynamic environments to identify the activity of
multiple users.
