# DomusFM: A Foundation Model for Event-Based Behavioral Monitoring in Smart-Homes

arXiv: https://arxiv.org/abs/2602.01910 · HTML: https://mlsanity.com/paper/2602.01910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 91 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters](https://mlsanity.com/paper/2308.08469/) (2023)
- [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023)
- [Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data](https://mlsanity.com/paper/2206.02909/) (2022)
- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [SensorLM: Learning the Language of Wearable Sensors](https://mlsanity.com/paper/2506.09108/) (2025)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [UniMTS: Unified Pre-training for Motion Time Series](https://mlsanity.com/paper/2410.19818/) (2024)
- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [EchoFM: Foundation Model for Generalizable Echocardiogram Analysis](https://mlsanity.com/paper/2410.23413/) (2024)
- [Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes](https://mlsanity.com/paper/2111.12158/) (2021)
- [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024)
- [Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity](https://mlsanity.com/paper/2306.13735/) (2023)
- [Utilizing Language Models for Energy Load Forecasting](https://mlsanity.com/paper/2310.17788/) (2023)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [In-Depth Behavior Understanding and Use: The Behavior Informatics Approach](https://mlsanity.com/paper/2007.15516/) (2020)
- [MuRAL: A Multi-Resident Ambient Sensor Dataset Annotated with Natural Language for Activities of Daily Living](https://mlsanity.com/paper/2504.20505/) (2025)
- [CARE: Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams](https://mlsanity.com/paper/2510.16988/) (2025)
- [Knowledge Distillation for LLM-Based Human Activity Recognition in Homes](https://mlsanity.com/paper/2601.07469/) (2026)
- [Improving Zero-shot ADL Recognition with Large Language Models through Event-based Context and Confidence](https://mlsanity.com/paper/2601.08241/) (2026)
- [Kalman-Like Filter under Binary Sensors](https://mlsanity.com/paper/2110.14264/) (2021)

## Abstract (arXiv)

Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes. In this setting, tasks like activity recognition, prediction, and pattern discovery provide complementary views of daily life, supporting the modeling of personal routines and habits, and their long-term changes. Existing approaches, however, face critical limitations. Supervised approaches require impractical amounts of labeled activity data to capture the variability of daily behavior across residents and environments. Foundation models represent a promising direction for learning transferable representations of latent behavioral patterns from sensor data. Still, current efforts are mostly designed for continuous inertial or physiological sensor data and do not address the sparse, discrete, and semantically rich event streams produced by smart homes. In this paper, we introduce DomusFM, a domain-specific foundation model for sensor-based behavioral monitoring in smart homes based on semantic event streams. DomusFM employs a self-supervised dual contrastive learning paradigm to capture both event-level semantic attributes and sequence-level temporal dependencies. By integrating semantic embeddings from a lightweight language model and specialized encoders for temporal patterns and binary states, DomusFM learns transferable representations that can be adapted across heterogeneous smart-home environments and tasks related to activity and event analysis. Through a leave-one-dataset-out evaluation across seven public smart-home datasets, we demonstrate that DomusFM consistently outperforms baselines on three downstream tasks: ADL recognition, next-k event prediction, and unsupervised clustering. DomusFM has a small footprint and can be deployed on edge devices.