US 12,111,890 B2
Detecting abnormal human behavior by using data from user's portable devices
Teodora Sandra Buda, Barcelona (ES); Iñaki Estella Aguerri, Barcelona (ES); Mohammed Khwaja, Bangalore (IN); Roger Garriga Calleja, Barcelona (ES); and Aleksandar Matic, Lloret de Mar (ES)
Assigned to KOA HEALTH DIGITAL SOLUTIONS S.L.U., Barcelona (ES)
Filed by KOA HEALTH DIGITAL SOLUTIONS S.L.U., Barcelona (ES)
Filed on Dec. 2, 2021, as Appl. No. 17/540,853.
Application 17/540,853 is a continuation in part of application No. PCT/EP2020/085724, filed on Dec. 11, 2020.
Claims priority of application No. 19383093 (EP), filed on Dec. 11, 2019.
Prior Publication US 2022/0095081 A1, Mar. 24, 2022
Int. Cl. G06F 18/2433 (2023.01); H04W 4/029 (2018.01)
CPC G06F 18/2433 (2023.01) [H04W 4/029 (2018.02)] 19 Claims
OG exemplary drawing
 
15. A system for detecting abnormal human behavior, the system comprising:
a memory that stores computer-readable code; and
a processor operatively coupled to the memory, wherein the processor is configured to implement the computer-readable code to:
define time intervals applicable to a user based on historical data from sensors;
personalize the time intervals based on data actively input by the user or real-time data from the sensors;
construct words for each of the time intervals, wherein a word is constructed as a vector that includes one of the time intervals and a plurality of discrete sensor-based feature levels, wherein each of the discrete sensor-based feature levels is mapped to a range of values of a sensor-based feature that are extracted from the data from the sensors;
construct a text document from the words based on the time intervals and the discrete sensor-based feature levels;
extract routines for each of the time intervals by extracting the words that most frequently appear in the text document using topic modeling to obtain a routine vector, wherein the routines are characterized by the discrete sensor-based feature levels associated with the most frequently appearing words; and
generate an alert if the sensor-based feature extracted in a current time interval deviates from a past routine extracted for a past time interval prior to the current time interval.