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

TL;DR

SGSM is a semi-generalist sensing model inspired by foundation models, capable of handling diverse sensing tasks with less labeled data, and demonstrating broad applicability and improved performance over specialized solutions.

Contribution

The paper introduces SGSM, a novel semi-generalist sensing model that can adapt to multiple sensing modalities and tasks with minimal task-specific data, advancing intelligent sensing systems.

Findings

SGSM achieves broad applicability across acoustic and Wi-Fi sensing.

In Wi-Fi sensing, SGSM improves accuracy by 20%.

SGSM outperforms some sensor-specific solutions in certain scenarios.

Abstract

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.