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
- URL: http://arxiv.org/abs/2202.08146v4
- Date: Tue, 9 May 2023 06:44:54 GMT
- Title: 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
- Authors: Md. Mohi Uddin Khan, Abdullah Bin Shams and Md. Mohsin Sarker Raihan
- Abstract summary: This study uses a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information for mutual human-to-human concurrent interaction recognition.
The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Human Activity Recognition (HAR) research has gained significant momentum due
to recent technological advancements, artificial intelligence algorithms, the
need for smart cities, and socioeconomic transformation. However, existing
computer vision and sensor-based HAR solutions have limitations such as privacy
issues, memory and power consumption, and discomfort in wearing sensors for
which researchers are observing a paradigm shift in HAR research. In response,
WiFi-based HAR is gaining popularity due to the availability of more
coarse-grained Channel State Information. However, existing WiFi-based HAR
approaches are limited to classifying independent and non-concurrent human
activities performed within equal time duration. Recent research commonly
utilizes a Single Input Multiple Output communication link with a WiFi signal
of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as
transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input
Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with
the time-series Wi-Fi channel state information based on 2.4 GHz channel
frequency for mutual human-to-human concurrent interaction recognition. The
proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network
(Attention-BiGRU) deep learning model can classify 13 mutual interactions with
a maximum benchmark accuracy of 94% for a single subject-pair. This has been
expanded for ten subject pairs, which secured a benchmark accuracy of 88% with
improved classification around the interaction-transition region. An executable
graphical user interface (GUI) software has also been developed in this study
using the PyQt5 python module to classify, save, and display the overall mutual
concurrent human interactions performed within a given time duration. ...
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