Google Inc.

Anupam Pathak

Dongeek Shin

Shwetak N Patel

Michael D Dixon

Todd Whitehurst

Brandon Charles Barbello

Rizwan Ahmed Chaudhry

Chetan Pitambar Bhole

Vaibhav Darbari

Jian Cui

A method for facilitating a Parkinson's Disease ('PD') assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.

Movement disorder diagnostics from video data using body landmark tracking

Various arrangements for performing radar-based measurement of vital signs. Waveform data may be received then filtered of data indicative of static objects to obtain motion-indicative waveform data. The motion-indicative waveform data may be analyzed to determine one or more frequencies of movement present within the motion-indicative waveform data. A spectral analysis may be performed on the motion-indicative waveform data to determine a spectral-analysis state of a monitored region. The spectral-analysis state of the monitored region may be determined to match a predefined spectral-analysis state during which vital sign monitoring is permitted. One or more vital signs of a monitored user present within the monitored region may be determined and output based on analyzing the motion-indicative waveform data.

Sleep tracking and vital sign monitoring using low power radio waves

Various arrangements for performing fall detection are presented. A smart-home device (), comprising a monolithic radar integrated circuit (), may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person () has occurred. Speech may then be output announcing that the fall has been detected via the speaker () of the smart home device ().

Privacy-preserving radar-based fall monitoring

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San Carlos, CA, United States of America

Anupam Pathak