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. ...

翻译：人类活动识别（HAR）研究因近期技术进步、人工智能算法发展、智慧城市需求及社会经济转型而获得显著发展动力。然而，现有基于计算机视觉和传感器的HAR解决方案存在隐私问题、内存与功耗限制、传感器佩戴不舒适等局限性，研究者正见证HAR研究的范式转变。为此，基于Wi-Fi的HAR因能获取更粗粒度的信道状态信息而日益受到关注。然而，现有基于Wi-Fi的HAR方法局限于对等时长内独立且非并发的人类活动进行分类。近期研究通常利用5 GHz信道频率的Wi-Fi信号，采用单输入多输出通信链路，使用两个Wi-Fi路由器或两个Intel 5300网卡作为收发端。本研究则采用Wi-Fi路由器与Intel 5300网卡之间的多输入多输出无线链路，基于2.4 GHz信道频率的时域Wi-Fi信道状态信息，实现人与人之间的并发交互识别。所提出的自注意力引导双向门控循环神经网络（Attention-BiGRU）深度学习模型能够对13种相互交互进行分类，在单对受试者场景下达到94%的最高基准准确率。该模型扩展至十对受试者后，在交互过渡区域实现分类改进，获得88%的基准准确率。本研究还基于PyQt5 Python模块开发了可执行的图形用户界面（GUI）软件，用于对指定时段内发生的相互并发人类交互进行分类、保存与显示。...