We use multiple approaches to evaluate the accuracy of Hyfe’s cough detection algorithms and the performance of its apps in various real-world scenarios. In a clinical context, Hyfe has optimized its apps for high performance in quiet indoor settings, such as a bedroom or hospital room, for any user, and for all common smartphone models. This is the context for which performance tests are designed to evaluate.
Cough is a ubiquitous health indicator whose impact is extreme and underappreciated. As an information-rich symptom that is associated with a myriad of diseases, cough is also the ideal clinical endpoint. Yet, cough continues to be assessed mainly through subjective surveys, as it has been for decades, even centuries. More than half the world population now carries smartphones that can be used as clinical tools for quantifying cough endpoints objectively. Scalable mobile cough monitoring has the potential to transform clinical trials by increasing their success rate, decimating their costs, accelerating products to market, and improving health equity worldwide.
Until recently technology has lagged behind the need for effective cough monitoring solutions. Now, with the parallel rise of smartphone use and machine learning analytics, half the world’s population is already carrying the device that can fundamentally change our approach to the global crisis in respiratory health.
Cough is an information-rich and readily monitored syndrome that will pioneer ’smart risk profiling’ systems, prove their value in alleviating the global burden of respiratory disease, and usher in a new era of proactive mobile health.