SenSE: Multimodal Biometric Sensor for Optimal Regulation of Circadian Rhythm and Neurocognitive Performance

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Circadian rhythms are regulated by an internal biological clock that synchronizes our biological processes with the daily light and dark pattern. It regulates sleep, metabolism, hormone secretion, and neurobehavioral processes that impact alertness and work productivity. Disruption of circadian rhythms has negative impacts on health. Modern lifestyle poses challenges in maintaining healthy circadian regulation, such as exposure to bright light during nighttime and, more recently, the working-from-home situation that blurs the boundary between work and personal time. This problem can be addressed by involving smart and connected built environments that promote individual circadian health and work productivity. The goal of this project is to obtain reliable mathematical models that capture the dynamics of individual circadian rhythms and the ability to estimate the state of circadian rhythms and related neurobehavioral processes. This project will build wearable hardware and software to extract useful information from noisy biometric signals that can be used in building the above-mentioned mathematical model and state estimation. The software will be deployed as a smartphone app that will use this information, interface with the human users, and provide optimal recommendations for sleep, lighting, and task schedules to maintain healthy circadian rhythms and optimize work productivity. On the educational front, the project will support the training of graduate and undergraduate students in multidisciplinary research, integration of new pedagogical material into the engineering design curriculum, and outreach activities to raise interest in STEM among student populations that are currently underrepresented in STEM fields. The clinical standard for assessing the state of the circadian system is by measuring biomarkers such as the concentration of hormones that participate in circadian rhythm regulation. Such procedures are impractical for online use in a closed-loop feedback system. Off-the-shelf wearable devices can only partially fill the need for online personalized biometric measurements because they only measure a limited set of signals and exclude the critical measurement on blue light exposure. In this project, the investigators will develop wearable sensor devices that (1) have energy harvesting capability, (2) measure indirect circadian phase markers such as actigraphy, body temperature, heart/pulse rate variation, blood pressure, ECG, and EEG, and (3) can resolve the spectral content of blue light exposure to the subject. The investigators will develop signal processing algorithms for noisy heterogeneous biometric data from various sensing modalities developed in this project. The processed signals will be used in model identification and state estimation of the circadian system and related neurocognitive processes. The state estimation algorithms will use tools from control theory and machine learning and combine model-free and model-based approaches to achieve robustness to noise and data dropouts. The validity of the hardware and software developed in this project will be evaluated in controlled in-lab experiments with the assessment of dim light melatonin onset in healthy human subjects.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.