The SensorFM model treats wearable sensor data as a language to predict health trends. By training on diverse time-series signals, it creates a general interface for physiological monitoring. This research moves beyond task-specific models toward a unified foundation. Practitioners can now apply generative pre-training to irregular health telemetry for better diagnostic accuracy.