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

Intelligent sensing systems have shown remarkable performance on many environmental perception (e.g., liquid recognition [1], soil moisture estimation [2], temperature monitoring [3]) and human activity (e.g., fall detection [4], vital sign estimation [5], location tracking [6]) tasks, becoming the core component of smart physical-related services, such as smart city and smart manufacturing. However, the current cost of designing intelligent sensing systems is relatively high since the models were designed to solve specific tasks with expensive expert knowledge [7] or a substantial amount of domain-specific data [8], one at a time. Foundation models [9] - the latest generation of artificial intelligence (AI) models - are intuitively used to generalize the model for numerous downstream tasks, which are trained on large multimodal datasets. They can solve entirely new tasks which the models are never explicitly trained for. Although the foundation models paradigm perform well in computer vision or natural language processing area, applying them in the intelligent sensing area is still challenging for two reasons. First, it is difficult to generate or access massive and diverse sensing datasets. Massive high-quality data is crucial for foundation model applications, such as computer vision [10] and natural language processing [9]. However, this requirement is often unmet in the sensing field.