Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Prospective Approach for Human-to-Human Interaction Recognition from
  Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural
  Network with GUI Application Implementation   Recent advances in 5G wireless technology and socioeconomic transformation
have brought a paradigm shift in sensor applications. Wi-Fi signal demonstrates
a strong correlation between its temporal variation and body movements, which
can be leveraged to recognize human activity. In this article, we demonstrate
the cognitive ability of device free mutual human-to-human interaction
recognition method based on the time scale Wi-Fi channel state information. The
mutual activities examined are steady-state, approaching, departing,
handshaking, high-five, hugging, kicking (left-leg), kicking (right-leg),
pointing (left-hand), pointing (right-hand), punching(left-hand), punching
(right-hand), and pushing. We explore and propose a Self-Attention furnished
Bidirectional Gated Recurrent Neural Network model to classify the 13
interaction types from the time-series data through automated temporal feature
extraction. Our proposed model can recognize a two subject pair mutual
interaction with a maximum benchmark accuracy of 94%. This has been expanded
for ten subject pairs, which secured a benchmark accuracy of 88% with improved
classification around the interaction-transition region. Also, an executable
graphical user interface (GUI) is developed, using the PyQt5 python module, to
subsequently display the overall mutual human-interaction recognition procedure
in real-time. Finally, we conclude with a brief discourse regarding the
possible solutions to the handicaps that resulted in curtailments observed
during the study. Such, Wi-Fi channel perturbation pattern analysis is believed
to be an efficient, economical & privacy-friendly approach to be potentially
utilized in mutual human-interaction recognition for indoor activity
monitoring, surveillance system, smart health monitoring systems & independent
assisted living.

33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th