US 12,446,811 B2
Biosignal integration with vehicles and movement
Philip Low, Hillsborough, CA (US)
Assigned to Neurovigil, Inc., Moffett Field, CA (US)
Filed by NEUROVIGIL, INC., Moffett Field, CA (US)
Filed on Sep. 9, 2024, as Appl. No. 18/828,811.
Application 18/828,811 is a continuation of application No. PCT/US2024/045698, filed on Sep. 6, 2024.
Claims priority of provisional application 63/581,367, filed on Sep. 8, 2023.
Claims priority of provisional application 63/581,244, filed on Sep. 7, 2023.
Prior Publication US 2025/0083680 A1, Mar. 13, 2025
Int. Cl. B60W 60/00 (2020.01); A61B 5/00 (2006.01); A61B 5/11 (2006.01); A61B 5/18 (2006.01); A61B 5/257 (2021.01); B60N 2/00 (2006.01); B60N 2/02 (2006.01); B60N 2/806 (2018.01); B60Q 9/00 (2006.01); B60W 40/08 (2012.01); B60W 50/00 (2006.01); G06F 3/14 (2006.01); G06V 20/59 (2022.01); G10L 15/22 (2006.01); H04N 7/18 (2006.01); H04W 4/40 (2018.01); H04W 4/80 (2018.01); B60H 1/00 (2006.01)
CPC A61B 5/18 (2013.01) [A61B 5/1103 (2013.01); A61B 5/257 (2021.01); A61B 5/4809 (2013.01); A61B 5/6893 (2013.01); B60N 2/0022 (2023.08); B60N 2/0252 (2013.01); B60N 2/026 (2023.08); B60N 2/0276 (2013.01); B60N 2/806 (2018.02); B60Q 9/00 (2013.01); B60W 40/08 (2013.01); B60W 50/0097 (2013.01); B60W 60/0016 (2020.02); B60W 60/0051 (2020.02); G06F 3/14 (2013.01); G06V 20/597 (2022.01); G10L 15/22 (2013.01); H04N 7/183 (2013.01); H04W 4/40 (2018.02); H04W 4/80 (2018.02); B60H 1/00742 (2013.01); B60W 2040/0872 (2013.01); B60W 2540/221 (2020.02); B60W 2540/225 (2020.02); B60W 2540/229 (2020.02); B60W 2556/10 (2020.02); B60W 2556/45 (2020.02); G10L 2015/223 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of operations comprising:
accessing one or more physiological signals of a subject sitting in a vehicle, wherein the one or more physiological signals are collected by a physiological data acquisition assembly that comprises a sensing device and one or more clusters of electrodes, and wherein each cluster of the one or more clusters of electrodes comprises at least an active electrode;
computing a set of features by using the one or more physiological signals for a given time interval, wherein the set of features comprises values that are derived from one or more frequency bands of the one or more physiological signals;
predicting one or more health metrics by using the set of features with one or more machine learning models, wherein the one or more health metrics corresponds to measures indicating a health status of the subject;
determining an extent to which the one or more health metrics are deviated from a corresponding baseline value; and
triggering one or more actions based on the determination, wherein the one or more actions include outputting the one or more health metrics, generating an alert signal to the subject or a caregiver, or adapting a vehicle control, wherein
the given time interval corresponds to a scanning window that is used to segment the one or more physiological signals;
the set of features is associated with the one or more frequency bands of the one or more physiological signals for the given time interval;
the set of features includes one or more of Delta power, Gamma power, standard deviation, maximum amplitude, Gamma power/Delta power, time derivative of Delta, and time derivative of the Gamma power/Delta power; and
the set of features further includes features that are derived using component analysis from a spectrogram or a normalized spectrogram of the one or more frequency bands of the one or more physiological signals for the given time interval.