  Human activity recognition (HAR) using ambient sensors in smart homes has
numerous applications for human healthcare and wellness. However, building
general-purpose HAR models that can be deployed to new smart home environments
requires a significant amount of annotated sensor data and training overhead.
Most smart homes vary significantly in their layouts, i.e., floor plans and the
specifics of sensors embedded, resulting in low generalizability of HAR models
trained for specific homes. We address this limitation by introducing a novel,
layout-agnostic modeling approach for HAR systems in smart homes that utilizes
the transferrable representational capacity of natural language descriptions of
raw sensor data. To this end, we generate Textual Descriptions Of Sensor
Triggers (TDOST) that encapsulate the surrounding trigger conditions and
provide cues for underlying activities to the activity recognition models.
Leveraging textual embeddings, rather than raw sensor data, we create activity
recognition systems that predict standard activities across homes without
either (re-)training or adaptation on target homes. Through an extensive
evaluation, we demonstrate the effectiveness of TDOST-based models in unseen
smart homes through experiments on benchmarked CASAS datasets. Furthermore, we
conduct a detailed analysis of how the individual components of our approach
affect downstream activity recognition performance.
