Activity Segmentation for Wireless Sensing
M. Sluijs (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.A. Zuñiga Zamalloa – Graduation committee member (TU Delft - Networked Systems)
F. Fioranelli – Graduation committee member (TU Delft - Microwave Sensing, Signals & Systems)
A. Asadi – Mentor (TU Delft - Embedded Systems)
F.M.L. Portner – Mentor (TU Delft - Embedded Systems)
S. Pintea – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
WiFi sensing enables non-intrusive, device-free monitoring of human activities by analyzing Channel State Information (CSI) extracted from commodity WiFi signals. While most research has studied Human Activity Recognition on pre-segmented clips, the harder problem of temporal activity segmentation — partitioning a continuous CSI stream into labeled activity intervals — has received less attention, and progress is limited by the absence of high-quality datasets and standardized evaluation infrastructure.
This thesis addresses that gap through three interconnected contributions. First, we introduce WiPos, a multimodal dataset in which a subject performs activities at freely varying positions, annotated with millisecond-scale precision using motion capture. Second, we present Breaking-CSI, a unified benchmarking framework that enables fair, reproducible comparison of segmentation methods across multiple datasets. Third, we propose DopplerTAS, a temporal activity segmentation model that operates on Doppler features derived from the time-differential CSI phase rather than raw amplitude, making predictions largely position-invariant.
Experiments using Breaking-CSI to evaluate representative baselines from the literature show that all of them suffer a consistent accuracy drop on WiPos compared to their native datasets, confirming that positional variation is the dominant challenge. DopplerTAS achieves 96.7% frame accuracy and 90.4% mIoU on WiPos, improving over 30 percentage points on both metrics.
Together, these contributions provide the dataset quality, evaluation thoroughness, and modeling approach needed to advance WiFi-based temporal activity segmentation from isolated recognition experiments toward continuous, position-robust sensing.