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.

使用环境传感器在智能家居中进行人类活动识别具有人类健康和福祉的多种应用。通过引入一种新颖的面向布局无关的智能家居人类活动识别系统建模方法，利用原始传感器数据的自然语言描述的可转移表示能力，我们在新的智能家居环境下构建通用的人类活动识别模型，并通过对基准 CASAS 数据集的实验验证了 TDOST 模型在未知智能家居中的有效性，并详细分析了我们方法的各个组成部分对下游活动识别性能的影响。