ADL-CLIP: Text-Aligned RF Representation for Continuous ADL Detection in Home Environments

Patterns of activities of daily living (ADLs) can provide early indicators of changes for many diseases and health conditions. Radio frequency (RF) sensing is promising for its contactless and privacy-preserving properties. However, most RF-based human activity recognition (HAR) studies focus on short, well-segmented actions, and their applicability to continuous, longer-duration ADLs remains largely uncertain. Existing continuous activity detection methods are typically trained in a closed set with labeled boundaries, limiting their ability to handle unseen, unsegmented ADLs in real life. In this work, we address both challenges by introducing ADL-CLIP, a framework for RF-based continuous ADL detection in home environments without pre-known boundaries and generalizable to unseen activities. We observe that intra-ADL radio frames cluster closely in the representation space, while those across ADLs are more separated. Building on this insight, we formulate ADL segmentation as a label-free optimization problem that identifies the boundaries by minimizing intra-segment while maximizing across-segment distances. To reconcile the discrepancy between data-driven segmentation and human annotated boundaries, we alternate between refining segmentation boundaries in the representation space and fine-tuning the representation model combining both boundaries using CLIP-style contrastive learning, progressively aligning the two while preserving prior knowledge from language models. We evaluate ADL-CLIP on a dataset collected from 46 non-researcher participants performing 25 ADLs continuously with 16 UWB sensors in an instrumented one-bedroom, one-bathroom apartment. In an open-set setting where 5 ADLs are held out during training, ADL-CLIP achieves 76.6% accuracy on all 25 ADLs, outperforming temporal activity detection baselines by 26% and NLS-based HAR by 34%. To our knowledge, this is the first RF-based open-set, boundary-free ADL detection study in an instrumented home environment.