AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments

A major obstacle in developing robust and generalizable smart home-based Human Activity Recognition (HAR) systems is the lack of large-scale, diverse labeled datasets. Variability in home layouts, sensor configurations, and user behavior adds further complexity, as individuals follow varied routines and perform activities in distinct ways. Building HAR systems that generalize well requires training data that captures the diversity across users and environments. To address these challenges, we introduce AgentSense, a virtual data generation pipeline where diverse personas are generated by leveraging Large Language Models. These personas are used to create daily routines, which are then decomposed into low-level action sequences. Subsequently, the actions are executed in a simulated home environment called VirtualHome that we extended with virtual ambient sensors capable of recording the agents activities as they unfold. Overall, AgentSense enables the generation of rich, virtual sensor datasets that represent a wide range of users and home settings. Across five benchmark HAR datasets, we show that leveraging our virtual sensor data substantially improves performance, particularly when real data are limited. Notably, models trained on a combination of virtual data and just a few days of real data achieve performance comparable to those trained on the entire real datasets. These results demonstrate and prove the potential of virtual data to address one of the most pressing challenges in ambient sensing, which is the distinct lack of large-scale, annotated datasets without requiring any manual data collection efforts.

Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)

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.

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and/ or only few samples from a target application domain are available, each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework, Cross-Domain HAR, which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Through our extensive experimental evaluation on a range of benchmark datasets, we demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance.