STTR Phase I: Toward the first novel multi-sensor smart wearable system for hot flash detection

Award ID: 2111818

The broader impact/commercial potential of this Small Business Technology Transfer Program (STTR) Phase I project is to help women undergoing menopause. Most women undergo menopause, a complex biobehavioral life stage that spans one-third of their life, frequently accompanied by a complex array of symptoms affecting their health, quality of life, relationships, and productivity. The most common and disruptive menopause symptoms are hot flashes, affecting 80% of women, and persisting for up to 10-20 years, with an average duration of 7 years. Unfortunately, there is a lack of adequate support for menopause symptom management, with very few physicians receiving adequate training in menopausal care and unable to provide appropriate care. Importantly, no current solution exists on the market to automatically measure hot flashes and other menopause symptoms over time, a critical limitation in tracking and managing womens health, and supporting wellness across this challenging life stage. In the U.S., over 50 million women ages 45-55 have hot flashes. The global market for hot flashes is estimated at $9.5B annually and includes alternative, non-hormonal, and hormonal treatment options, with the U.S. accounting for about half-or $4.75B of the total global hot flash market. The proposed technology aims to solve key scientific, technical, and commercial challenges, to build the first novel cost-effective accurate wearable system for menopausal hot flash detection. The proposed wearable system will be an integrated key component of a broader mobile-based digital platform for managing menopause symptoms. This Small Business Technology Transfer Program (STTR) Phase I project Phase I will allow the implementation and evaluation of the effectiveness of using wrist-based multi-sensors, common noninvasive sensors used in commercially available wearable devices, to automatically classify hot flashes, in real-time and free-living conditions (e.g., during sleep and wake, in the presence of noise signals). A combination of synthetic and real-world data will be used to achieve the aims. The system targets >90% accuracy in hot flash classification, across different behavioral, physiological and environmental conditions, against the gold standard method (expert evaluation of fluctuation in sternum skin conductance). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Henry Ahn