Contactless Sleep Sensing in Nest Hub

 By Google AI Blog - 2021-03-16

Description

Posted by Michael Dixon, Software Engineer and Reena Singhal Lee, Product Manager, Google Health People often turn to technology to manag...

Summary

  • People often turn to technology to manage their health and wellbeing, whether it is to record their daily exercise, measure their heart rate, or increasingly, to understand their sleep patterns.
  • The frequency spectrum of the reflected signal contains an aggregate representation of the distance and velocity of objects within the scene.
  • To validate the accuracy of the algorithm, we compared it to the gold-standard of sleep-wake determination, the polysomnogram sleep study, in a cohort of 33 “healthy sleepers” (those without significant sleep issues, like sleep apnea or insomnia) across a broad age range (19-78 years of age).
  • The model works by continuously extracting spectrogram-like features from the audio input and feeding them through a convolutional neural network classifier in order to estimate the probability that coughing or snoring is happening at a given instant in time.

 

Topics

  1. UX (0.3)
  2. NLP (0.15)
  3. Backend (0.13)

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