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STTR Phase I: Individualized circadian rhythm therapy via schedule-sensitive photic interventions in shared workspaces

STTR Phase I: Individualized circadian rhythm therapy via schedule-sensitive photic interventions in shared workspaces
STTR 第一阶段:通过共享工作空间中日程敏感的光干预进行个性化昼夜节律治疗
批准号:
2025864
负责人:
Eliza Van Reen
金额:
$25.47万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-12-01 至 2022-03-31

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英文摘要
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to support individuals with sleep disorders. Sleep and circadian rhythms influence human performance, mood, decision-making, learning, memory, alertness, and overall physical and mental health. The demand for humans to perform critical work at adverse circadian phases while lacking adequate sleep has played a role in some of the world’s most devastating industrial and engineering disasters, as well as “everyday” incidents in the workplace and on the roadways. The economic burden of sleep loss is estimated at $411 billion/year in the US. Providing light appropriately can help address these potentially dangerous situations. The proposed project combines hardware design (fixed and wearable sensors), systems engineering and circadian rhythm biology to provide individuals in shared spaces with targeted (primarily light-based) interventions to improve their circadian rhythmicity, sleep and general wellness. This Small Business Innovation Research (SBIR) Phase I project integrates wearable and stationary biometric circadian sensors for measuring the circadian state of each individual; indoor positioning sensors for tracking ambient occupation patterns; controllers for delivering light and other interventions; and algorithms for effectively managing conflicting and competing schedules. Shared spaces offer several unique and highly individualized challenges in terms of ensuring that the stimulus for one individual does not interfere with another person. The studies will validate the technology and provide data to train the algorithms.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.
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