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%。