Automotive Health 2.0: Steering Toward Proactive Preventive Care.

Area of Science:

• Biomedical Engineering
• Digital Health
• Preventive Medicine

Background:

• Current disease prevention is hampered by infrequent clinical assessments.
• Vehicles offer a consistent environment for continuous health monitoring.
• Existing in-cabin vehicle sensors can be repurposed for physiological data collection.

Purpose of the Study:

• To introduce Automotive Health 2.0, a framework for using connected vehicles as validated health monitoring platforms.
• To enable unobtrusive, continuous physiological sensing and proactive care delivery.
• To integrate vehicle-based health data with electronic health records for clinical use.

Main Methods:

• Utilizing in-cabin cameras, radar, and microphones for multimodal data acquisition.
• Developing algorithms to unobtrusively estimate cardiovascular, respiratory, and behavioral parameters.
• Applying artificial intelligence (AI) for disease signature detection and risk assessment.

Main Results:

• Demonstrated potential for AI-driven analytics to detect early disease signatures.
• Established a framework for regulatory-grade monitoring prioritizing accuracy and reproducibility.
• Proposed a scalable model for population-level health monitoring within vehicles.

Conclusions:

• Automotive Health 2.0 transforms vehicles into extensions of the healthcare ecosystem.
• This paradigm facilitates earlier disease detection and more equitable access to preventive care.
• It provides clinicians with continuous, actionable data for improved patient management and public health.