The significance of intelligent sensing systems is growing in the realm of
smart services. These systems extract relevant signal features and generate
informative representations for particular tasks. However, building the feature
extraction component for such systems requires extensive domain-specific
expertise or data. The exceptionally rapid development of foundation models is
likely to usher in newfound abilities in such intelligent sensing. We propose a
new scheme for sensing model, which we refer to as semi-generalist sensing
model (SGSM). SGSM is able to semiautomatically solve various tasks using
relatively less task-specific labeled data compared to traditional systems.
Built through the analysis of the common theoretical model, SGSM can depict
different modalities, such as the acoustic and Wi-Fi signal. Experimental
results on such two heterogeneous sensors illustrate that SGSM functions across
a wide range of scenarios, thereby establishing its broad applicability. In
some cases, SGSM even achieves better performance than sensor-specific
specialized solutions. Wi-Fi evaluations indicate a 20\% accuracy improvement
when applying SGSM to an existing sensing model.

智能传感系统的重要性在智能服务领域日益增长。我们提出了一种新的感知模型方案，称为半通用感知模型 (SGSM)，它能够使用相对较少的特定任务标记数据来解决各种任务。通过对共同的理论模型进行分析构建的 SGSM 能够描述不同的模态，如声学和 Wi-Fi 信号。实验结果表明，SGSM 能够在广泛的场景中发挥作用，甚至在某些情况下比传感器特定的专业解决方案表现更好。将 SGSM 应用于现有的感知模型时，Wi-Fi 评估显示精度提高了 20%。

SGSM: 一种类似基础模型的半广义感知模型

SGSM: A Foundation-model-like Semi-generalist Sensing Model

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 13
human-to-human mutual interaction types from the time-series data. 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 and privacy-friendly approach to be potentially utilized in mutual
human-interaction recognition for indoor activity monitoring, surveillance
system, smart health monitoring systems and independent assisted living.

本文提出了一种基于 Wi-Fi 信号的人体移动监测技术，通过开发神经网络模型，能够识别人际交互行为并在实时环境下呈现监测结果。

一种基于 Wi-Fi 信道数据的人对人交互识别的前瞻性方法，采用注意力双向门控循环神经网络和 GUI 应用程序实现

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