US 12,268,530 B2
Illness detection based on nervous system metrics
Gerald Pho, Somerville, MA (US); Kirstin Aschbacher, San Francisco, CA (US); Marco Altini, Amsterdam (NL); Harpreet Rai, San Francisco, CA (US); and Michael Chapp, Lafayete, CA (US)
Assigned to Oura Health Oy, Oulu (FI)
Filed by Oura Health Oy, Oulu (FI)
Filed on Jun. 24, 2021, as Appl. No. 17/357,562.
Claims priority of provisional application 63/116,981, filed on Nov. 23, 2020.
Claims priority of provisional application 63/049,405, filed on Jul. 8, 2020.
Claims priority of provisional application 63/043,892, filed on Jun. 25, 2020.
Prior Publication US 2021/0401314 A1, Dec. 30, 2021
Int. Cl. A61B 5/00 (2006.01); A61B 5/01 (2006.01); A61B 5/024 (2006.01); A61B 5/026 (2006.01); A61B 5/08 (2006.01); A61B 5/11 (2006.01); G16H 40/63 (2018.01); G16H 50/30 (2018.01)
CPC A61B 5/7275 (2013.01) [A61B 5/01 (2013.01); A61B 5/02405 (2013.01); A61B 5/026 (2013.01); A61B 5/08 (2013.01); A61B 5/1118 (2013.01); A61B 5/4806 (2013.01); A61B 5/4812 (2013.01); A61B 5/6826 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); A61B 5/742 (2013.01); A61B 5/7475 (2013.01); G16H 40/63 (2018.01); G16H 50/30 (2018.01); A61B 2560/029 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a wearable device configured to measure physiological data from a user, the physiological data comprising heart rate variability data measured from the user throughout a first time interval and a second time interval subsequent to the first time interval;
a user device communicatively coupled with the wearable device; and
one or more processors communicatively coupled with the wearable device and the user device, the one or more processors configured to:
receive heart rate variability data measured from the user via the wearable device, the heart rate variability data collected via the wearable device throughout the first time interval and the second time interval subsequent to the first time interval;
input the heart rate variability data into a first machine learning classifier, wherein the first machine learning classifier is configured to extract a set of features from the heart rate variability data;
identify, using the first machine learning classifier, root mean square of successive differences (RMSSD) data and resting heart rate data associated with the user based at least in part on the heart rate variability data,
input the set of features extracted by the first machine learning classifier into a second machine learning classifier, wherein the set of features extracted by the first machine learning classifier comprise the RMSSD data and the resting heart rate data;
identify, using the second machine learning classifier, a satisfaction of one or more deviation criteria between a first subset of the set of features associated with the first time interval and a second subset of the set of features associated with the second time interval, wherein the second machine learning classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on identifying a decrease in the RMSSD data and a decrease in the resting heart rate data occurring at approximately a same time within the second time interval; and
transmit instructions to a graphical user interface of the user device to cause the graphical user interface to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state due to a bacterial infection, a viral infection, or both.