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This paper introduces StepFM, a foundation model that utilizes step counter data for broad-spectrum health prediction, addressing the limitations of existing sensor models that are often disease-specific. By leveraging the low-dimensional and ubiquitous nature of step data, StepFM offers a scalable, privacy-preserving alternative that can be applied across diverse health risk tasks. Experimental results show that StepFM not only outperforms traditional methods but also reveals interpretable relationships between physical activity and health risks, enhancing our understanding of activity-based health modeling.
StepFM reveals that simple step data can outperform complex sensor models in predicting a wide range of health risks, making health monitoring more accessible and privacy-friendly.
Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific disease types, limiting their transferability across different health risks. Wearable foundation models offer a more generalizable approach in diverse health risk types. Nevertheless, most existing methods rely on high-frequency raw sensor data, raising concerns about privacy, computational overhead, and scalability across devices and populations. In this paper, we propose StepFM, a foundation model built solely on step counter data for broad-spectrum health prediction. Leveraging the ubiquity and low-dimensional nature of step data, StepFM provides a practical, privacy-preserving, and computation-efficient alternative to traditional sensor-based models. We design a scalable pre-training framework that captures temporal dynamics and behavioral patterns from large-scale step sequences, enabling transfer across more than 20 health risk prediction tasks spanning diverse devices, new regions, and novel disease types. Extensive experiments demonstrate that StepFM achieves strong performance compared to existing methods while maintaining robustness across heterogeneous settings. Furthermore, our analysis reveals interpretable and generalizable relationships between physical activity patterns and various health risks, offering new insights into activity-based health modeling. Our work establishes step-based sensing as a viable foundation for scalable and real-world health monitoring.
