Sensor fusion for multi-parametric mast cell monitoring

No single signal tells the whole story of mast cell activation. This study shows how fusing multiple biosignal streams yields a more reliable picture than any one measurement.

Introduction

Individual physiological signals are noisy and easily confounded. Sensor fusion combines complementary streams to separate genuine activation from ordinary variation.

Methodology

We integrated temperature, cardiovascular, and inflammatory-associated signals within a unified model evaluated against labeled activation events.

Fused signals:

• Skin and ambient temperature dynamics
• Heart rate and variability
• Inflammatory and activation-associated markers
• Motion and context signals

Results

Fused models detected activation events with fewer false positives than any single-signal approach, particularly during activity and heat.

Clinical Implications

Combining signals improves reliability, which is essential for alerts patients can act on without alarm fatigue.

Conclusion

Sensor fusion is a promising foundation for robust, real-world mast cell monitoring.