In the future occupant monitoring systems will provide low-cost health screening to hundreds of millions of people. Vehicles will screen for physiological and neurological conditions, and provide health practitioners with a wealth of data to aid in proactive diagnosis. In the coming years the automotive industry will make health screening ubiquitous.
The automotive industry needs to be careful to employ the correct approach to bring this vision to light. Two broad approaches will be presented that are commonly used to build a feature into occupant monitoring systems: minimum viable product versus scientifically valid product. While the former approach is best suited for comfort features, the latter is required for health monitoring.
If the goal is to measure features such as health conditions, cognitive impairment, or attentiveness, the starting point should be to consult with domain experts and understand the problem space. Data scientists will then have clear guidance on the current state of the science and how best to translate that to an accurate, meaningful, and scientifically valid data model. If the industry adopts the minimum viable product approach and skips this critical step, it will hit an upper limit of accuracy resulting in misclassifications. This compromises the integrity of all later steps because of an erroneous foundation.
This talk advocates for the scientifically valid product approach. Practical examples of how partnering with institutes and domain experts in conducting clinical studies will be presented.