Patent Issued for Systems and methods for using primary and redundant devices for detecting falls (USPTO 11468758): Aetna Inc.

2022 OCT 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Kurfirst, Dwayne (Hartford, CT, US), filed on June 4, 2021, was published online on October 11, 2022.

The assignee for this patent, patent number 11468758, is Aetna Inc. (Hartford, Connecticut, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Individuals, especially elderly individuals, are very susceptible to falls, which may cause lasting injury and even death in some circumstances. Additionally, even if the individual does not fall, the risk of falling may cause anxiety or unnecessary stress to their caretakers or family members. For instance, if the fall is severe enough, the individual may lose consciousness or be in such a vulnerable state that they are unable to seek the attention that they require. A caretaker might not be able to be with the individual at all times and therefore, the individual may be late in receiving the proper medical attention. Even receiving medical attention a few minutes earlier may reduce the severity of the injury or even prevent death. Accordingly, there remains a technical need to provide a system that uses devices to detect an occurrence of a fall as well as to improve the response time if/when a fall occurs.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “In some examples, the present application may use multiple, redundant fall detection devices to detect a fall event (e.g., based on sensor information, the fall detection device may determine the individual has fallen). For example, the fall detection devices may provide information indicating the fall event to a user device. Based on this information, the user device may request feedback from the individual as to whether the individual has fallen. In some instances, if the user feedback indicates the individual has fallen, the user device may provide a notification to an emergency contact such as a caretaker or emergency personnel indicating the individual has fallen and requires assistance. In other instances, if the user feedback indicates the individual did not fall, the user device may provide update information to the fall detection devices to update their own fall detection models that are used with the sensor information to determine whether the individual has fallen. In other words, each fall detection device may have their own fall detection model that may be consistently updated and individualized for the particular individual based on the user feedback such that the fall detection model increases in the accuracy after each iteration.

“In one aspect, a fall detection system includes a first fall detection device and a user device. The first fall detection device includes: one or more sensors; one or more first processors; and a first non-transitory computer-readable medium having first processor-executable instructions stored thereon. The first processor-executable instructions, when executed, facilitate: detecting an occurrence of a fall event associated with an individual based on sensor information from the one or more sensors and a fall detection model; and providing, to a user device, a first indication indicating the occurrence of the fall event. The user device includes: one or more second processors; and a second non-transitory computer-readable medium having second processor-executable instructions stored thereon. The second processor-executable instructions, when executed, facilitate: receiving, from the first fall detection device, the first indication; based on the first indication and a second indication from a second fall detection device, causing display of a prompt requesting user feedback as to whether the individual fell; based on the user feedback, providing, to the first fall detection device, update information indicating for the first fall detection device to update the fall detection model; and based on the user feedback, providing, to a back-end server, user fall information associated with the occurrence of the fall event.

“Examples may include one of the following features, or any combination thereof. For instance, in some examples, the user feedback indicates the individual did not fall and the update information indicates for the first fall detection device to update the fall detection model based on the user feedback indicating that the individual did not fall. The first processor-executable instructions, when executed, further facilitate: updating the fall detection model based on the update information; and detecting an occurrence of a second fall event associated with the individual based on the updated fall detection model.

“In some instances, the fall detection system further comprises a second fall detection device comprising: one or more second sensors; one or more third processors; and a third non-transitory computer-readable medium having third processor-executable instructions stored thereon. The third processor-executable instructions, when executed, facilitate: detecting the occurrence of the fall event associated with the individual based on second sensor information from the one or more second sensors and a second fall detection model; and providing, to the user device, the second indication indicating the occurrence of the fall event.

“In some examples, the second processor-executable instructions, when executed, further facilitate: based on the user feedback, providing, to the second fall detection device, second update information indicating for the second fall detection device to update the second fall detection model based on the user feedback. The third processor-executable instructions, when executed, further facilitate: updating the second fall detection model based on the second update information; and detecting an occurrence of a second fall event associated with the individual based on the updated second fall detection model.”

The claims supplied by the inventors are:

“1. A fall detection system, comprising: an enterprise computing system, comprising: one or more first processors; and a first non-transitory computer-readable medium having first processor-executable instructions stored thereon, wherein the first processor-executable instructions, when executed, facilitate: training one or more machine learning (ML) datasets; obtaining, from a user device associated with an individual, user fall information indicating the individual has fallen; obtaining prescription information indicating one or more medical prescriptions for the individual; subsequent to training the one or more ML datasets, inputting the user fall information and the prescription information into the one or more ML datasets to determine causation information indicating whether the one or more medical prescriptions caused the individual to fall; and providing the causation information to a second computing device.

“2. The fall detection system of claim 1, wherein the user fall information comprises a user identifier (ID) indicating an identity of the individual, and wherein the first processor-executable instructions, when executed, facilitate: providing the user ID to a healthcare computing device, and wherein obtaining the prescription information is in response to providing the user ID to the healthcare computing device.

“3. The fall detection system of claim 1, wherein the user fall information comprises information indicating a time stamp associated with the individual falling, wherein the prescription information indicates a time for the individual to take a medication, and wherein inputting the user fall information and the prescription information into the one or more ML datasets comprises inputting the time stamp and the time for the individual to take the medication into the one or more ML datasets to determine the causation information.

“4. The fall detection system of claim 1, further comprising: a fall detection device comprising: one or more second processors; and a second non-transitory computer-readable medium having second processor-executable instructions stored thereon, wherein the second processor-executable instructions, when executed, facilitate: detecting an occurrence of a fall event associated with the individual based on first sensor information from one or more sensors and a fall detection model; and providing, to the user device, an indication indicating the occurrence of the fall event; and the user device comprising: one or more third processors; and a third non-transitory computer-readable medium having third processor-executable instructions stored thereon, wherein the third processor-executable instructions, when executed, facilitate: based on the indication, causing display of a prompt requesting user feedback as to whether the individual fell; based on the user feedback, providing, to the fall detection device, update information indicating for the fall detection device to update the fall detection model.

“5. The fall detection system of claim 4, wherein the fall detection model is a Hidden Markov Model (HMM).

“6. The fall detection system of claim 5, wherein the first sensor information comprises movement information indicating movement of the individual and height information indicating a height corresponding to the fall detection device, and wherein detecting the occurrence of the fall event comprises: inputting the movement information and the height information into the HMM to determine the occurrence of the fall event.

“7. The fall detection system of claim 5, wherein the HMM comprises a plurality of coefficients associated with a transition probability and an emission probability, and wherein detecting the occurrence of the fall event is based on the transition probability and the emission probability of the HMM.

“8. The fall detection system of claim 7, wherein the second processor-executable instructions, when executed, further facilitate: updating, based on the update information, the plurality of coefficients associated with the transition probability and the emission probability.

“9. The fall detection system of claim 8, wherein the update information comprises a plurality of updated coefficients for updating the HMM.

“10. The fall detection system of claim 4, wherein the fall detection model is a Monte Carlo Simulation Model.

“11. The fall detection system of claim 10, wherein the first sensor information comprises movement information indicating movement of the individual and height information indicating a height corresponding to the fall detection device, and wherein detecting the occurrence of the fall event comprises: inputting the movement information and the height information into the Monte Carlo Simulation Model to determine the occurrence of the fall event.

“12. The fall detection system of claim 4, further comprising: one or more environmental sensors configured to: detect environmental sensor information associated with the individual; and provide the environmental sensor information to the user device, and wherein the third processor-executable instructions, when executed, further facilitate: receiving the environmental sensor information from the one or more environmental sensors, and wherein causing display of the prompt requesting the user feedback as to whether the individual fell is further based on the environmental sensor information.

“13. The fall detection system of claim 12, wherein the one or more environmental sensors comprise one or more pressure sensors interwoven into a floor of a residence of the individual.

“14. The fall detection system of claim 12, wherein the one or more environmental sensors comprise one or more light motion sensors.

“15. The fall detection system of claim 12, wherein the one or more environmental sensors comprise one or more active sonar distance sensors.

“16. A method, comprising: training, by a fall detection system, one or more machine learning (ML) datasets; obtaining, by the fall detection system and from a user device associated with an individual, user fall information indicating the individual has fallen; obtaining, by the fall detection system, prescription information indicating one or more medical prescriptions for the individual; subsequent to training the one or more ML datasets, inputting, by the fall detection system, the user fall information and the prescription information into the one or more learning ML datasets to determine causation information indicating whether the one or more medical prescriptions caused the individual to fall; and providing, by the fall detection system, the causation information to a second computing device.

“17. The method of claim 16, wherein the user fall information comprises a user identifier (ID) indicating an identity of the individual, and wherein the method further comprises: providing the user ID to a healthcare computing device, and wherein obtaining the prescription information is in response to providing the user ID to the healthcare computing device.

“18. The method of claim 16, wherein the user fall information comprises information indicating a time stamp associated with the individual falling, wherein the prescription information indicates a time for the individual to take a medication, and wherein inputting the user fall information and the prescription information into the one or more ML datasets comprises inputting the time stamp and the time for the individual to take the medication into the one or more ML datasets to determine the causation information.

“19. A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate: training one or more machine learning (ML) datasets; obtaining, from a user device associated with an individual, user fall information indicating the individual has fallen; obtaining prescription information indicating one or more medical prescriptions for the individual; subsequent to training the one or more ML datasets, inputting the user fall information and the prescription information into the one or more ML datasets to determine causation information indicating whether the one or more medical prescriptions caused the individual to fall; and providing the causation information to a second computing device.”

For more information, see this patent: Kurfirst, Dwayne. Systems and methods for using primary and redundant devices for detecting falls. U.S. Patent Number 11468758, filed June 4, 2021, and published online on October 11, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11468758.PN.&OS=PN/11468758RS=PN/11468758

(Our reports deliver fact-based news of research and discoveries from around the world.)