A method for characterizing daily physiology from widely used wearables

Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the "Social Rhythms" iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method.

Focusing through dynamic scattering media

We demonstrate steady-state focusing of coherent light through dynamic scattering media. The phase of an incident beam is controlled both spatially and temporally using a reflective, 1020-segment MEMS spatial light modulator, using a coordinate descent optimization technique. We achieve focal intensity enhancement of between 5 and 400 for dynamic media with speckle decorrelation time constants ranging from 0.4 seconds to 20 seconds. We show that this optimization approach combined with a fast spatial light modulator enables focusing through dynamic media. The capacity to enhance focal intensity despite transmission through dynamic scattering media could enable advancement in biological microscopy and imaging through turbid environments.