SGSM: A Foundation-model-like Semi-generalist Sensing Model
This work addresses the need for more efficient and broadly applicable sensing models in smart services, representing an incremental advancement by adapting foundation model concepts to sensing tasks.
The paper tackles the problem of building feature extraction components for intelligent sensing systems, which typically require extensive domain expertise or data, by proposing a semi-generalist sensing model (SGSM) that can solve various tasks with less labeled data and achieve up to 20% accuracy improvement in Wi-Fi evaluations.
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.