SEGALL: A Unified Active Learning Framework for Wireless Sensing Data Segmentation

Abstract

Wireless sensing has emerged as a promising technology due to its inherently privacy-preserving and contactless characteristics, enabling a wide range of applications. Until now, a major challenge in translating research into real-world applications is achieving accurate data segmentation. This process involves identifying the start and end points of target activities within complex and extended time-series sensing data, forming the foundation for subsequent inference tasks. However, existing segmentation techniques in wireless sensing are often constrained to a specific signal type and exhibit limited performance in distinguishing fine-grained activities, particularly when faced with similar background signals. To address these issues, we present SegALL, a unified segmentation framework capable of processing diverse signals. By leveraging a lightweight, signal-independent deep learning architecture coupled with active learning, SegALL enables accurate segmentation for fine-grained activities, even under interference from activities with similar characteristics. Experimental evaluations on both synthetic and real-world datasets demonstrate that SegALL significantly improves segmentation reliability across various modalities, including IMU, Wi-Fi, and mmWave signals. For example, it achieves a segmentation accuracy of 91.12% for mmWave signals, outperforming previous segmentation solutions that achieved 70.50%. Furthermore, SegALL reduces manual labeling effort by 40.15% and maintains a lightweight computational cost, making it suitable for deployment on edge devices such as the Raspberry Pi.