SYSTEMS AND METHODS FOR UTILIZING WIRELESS PHYSIOLOGICAL SENSORS

• US 20160354039A1
• Filed: 06/27/2016
• Published: 12/08/2016
• Est. Priority Date: 03/05/2004
• Status: Active Grant

• Current Assignee: Terumo Medical Corporation (Terumo Corporation)
• Original Assignee: Health Outcomes Sciences, Inc. (Terumo Corporation)
• Inventors: Soto, Gabriel Enrique, Spertus, John Albert
• Granted Patent: US 10,806,404 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02444 Details of sensor A61B5/02427 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/6846 specially adapted to be brought in contact with an internal body part, i.e. invasive
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/00 : ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Physiological sensors may be utilized to obtain physiological data for a user. The sensor data may be utilized in predicting a user'"'"'s outcome to a medical intervention using one or more models. The models may be automatically executed in response to receiving certain types and/or amount of data, such as data received from one or more physiological remote sensors, such as Internet of Things sensors. The sensors may include heart rate sensors, arterial pressure sensors, glucose sensors, temperature sensors, weight sensors, blood oxygen sensors, urine sensors, saliva sensors, skin conduction sensors, muscle sensors, brain signal sensors, and/or other sensors. A sensor may communicate over the 2360-2400 MHz and/or the 30-37.5 MHz radio frequency (RF) band. The data may be received from a networked data store. Execution of the models may identify health issues in substantially real time, and the operation of one or more medical devices may be modified and/or a communication may be generated.

Claims

1. A sensor system, comprising;

a sensor interface configured to receive sensor signals from a plurality of physiological wireless sensors;

one or more computing devices;

a non-transitory memory medium that stores instructions that when executed by the one or more computing devices causes the sensor system to perform operations comprising;

establish a virtual private network;

receive over the virtual private network sensor data from one or more physiological wireless sensors associated with a first user, the physiological wireless sensors comprising at least a heart rate sensor that provides heart rate sensor data for the user, an arterial pressure sensor that provides arterial pressure sensor data for the user, a glucose sensor that provides glucose sensor data for the user, a blood oxygen sensor that provides blood oxygen sensor data for the user, or a body temperature sensor that provides body temperature sensor data for the user;

determine, based at least in part on the received sensor data, whether a first biological event predictive model is to be executed;

at least partly in response to determining that the first biological event predictive model is to be executed, execute the first biological event predictive model using at least a portion of the received sensor data to provide a biological prediction for the user;

based at least in part on the biological prediction for the user, determine whether an operation of a first medical device of the user is to be modified;

at least partly in response to determining that the operation of the first medical device of the user is to be modified, cause the operation of a first medical device of the user to be modified;

at least partly in response to determining that the operation of the first medical device of the user is not to be modified, leave the operation of the first medical device of the user unmodified.

2. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors comprises a biomedical microelectromechanical (MEMs) system configured to perform sample preconditioning and output preconditioned sample data.

3. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors is configured to communicate over a 2360-2400 MHz radio frequency band and/or a 30-37.5 MHz radio frequency band.

4. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors is assigned a unique Internet Protocol address.

5. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors comprises an implanted sensor or a sensor surface-mounted on a body of the user in a fixed position.

6. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors is worn using a wrist band.

7. The sensor system as defined in claim 1, wherein at least one of the physiological wireless sensors comprises a processing device, a battery, and a wireless transceiver.

8. The sensor system as defined in claim 1, wherein the sensor system is configured to:

dynamically generate a data interface to collect a plurality of types of health status information, demographic information, and clinical information, wherein the interface comprises a human-readable interface and/or a machine-readable interface;

collect the plurality of types of health status information, demographic information, and clinical information using the data interface;

identify potentially significant predictors of future health states based at least in part on information collected using the data interface;

generate the first biological event predictive model using the identified potentially significant predictors of future health states.

9. The sensor system as defined in claim 1, wherein the sensor system is configured to:

generate treatment recommendations based at least in part on generated predictor results obtained from executing the first biological event predictive model.

10. A method comprising:

receiving over a virtual private network at a computer system comprising a computing device encrypted sensor data from one or more physiological sensors associated with a first user, the physiological sensors comprising at least a heart rate sensor that provides heart rate sensor data for the user, an arterial pressure sensor that provides arterial pressure sensor data for the user, a glucose sensor that provides glucose sensor data for the user, a blood oxygen sensor that provides blood oxygen sensor data for the user, or a body temperature sensor that provides body temperature sensor data for the user;

determining by the computer system, based at least in part on the received sensor data, whether a first biological event predictive model is to be executed;

at least partly in response to determining that the first biological event predictive model is to be executed, executing by the computer system the first biological event predictive model using at least a portion of the received sensor data to provide a biological prediction for the user;

based at least in part on the biological prediction for the user, determining by the computer system whether a first action is to be taken, the first action comprising transmission of an electronic notification and/or a modification of an operation of a first medical device;

at least partly in response to determining that the first action is to be taken, transmitting by the computer system the electronic notification and/or causing the operation of a first medical device of the user to be modified; and

at least partly in response to determining that the first action is not to be taken, not taking, by the computer system the first action.

11. The method as defined in claim 10, wherein at least one of the physiological sensors comprises a biomedical microelectromechanical (MEMs) system configured to perform sample preconditioning and output preconditioned sample data.

12. The method as defined in claim 10, wherein at least one of the physiological sensors is assigned a unique Internet Protocol address.

13. The method as defined in claim 10, wherein at least one of the physiological sensors comprises an implanted sensor or a sensor surface-mounted on a body of the user in a fixed position.

14. The method as defined in claim 10, wherein at least one of the physiological sensors is worn using a wrist band.

15. The method as defined in claim 10, wherein at least one of the physiological sensors comprises a processing device, a battery, and a transceiver.

16. The method as defined in claim 10, the method further comprising:

dynamically generating a data interface to collect a plurality of types of health status information, demographic information, and clinical information, wherein the interface comprises a human-readable interface and/or a machine-readable interface;

collecting the plurality of types of health status information, demographic information, and clinical information using the data interface;

identifying potentially significant predictors of future health states based at least in part on information collected using the data interface;

generating the first biological event predictive model using the identified potentially significant predictors of future health states.

17. The method as defined in claim 10, the method further comprising:

generate treatment recommendations based at least in part on generated predictor results obtained from executing the first biological event predictive model.

18. A system, comprising;

a sensor interface configured to receive sensor signals from one or more physiological wireless sensors;

one or more computing devices;

a non-transitory memory medium that stores instructions that when executed by the one or more computing devices causes the system to perform operations comprising;

receive over a network sensor data from one or more physiological sensors associated with a first user, the one or more physiological sensors comprising at least a heart rate sensor that provides heart rate sensor data for the user, an arterial pressure sensor that provides arterial pressure sensor data for the user, a glucose sensor that provides glucose sensor data for the user, a blood oxygen sensor that provides blood oxygen sensor data for the user, or a body temperature sensor that provides body temperature sensor data for the user;

determine, based at least in part, on the received sensor data whether a first biological event predictive model is to be executed;

at least partly in response to determining that the first biological event predictive model is to be executed, execute the first biological event predictive model using at least a portion of the received sensor data to provide a biological prediction for the user;

based at least in part on the biological prediction for the user, determine whether a first action is to be taken, the first action comprising transmission of an electronic notification and/or a modification of an operation of a first medical device;

at least partly in response to determining that the first action is to be taken, transmit the electronic notification and/or cause the operation of a first medical device of the user to be modified.

19. The system as defined in claim 18, wherein at least one of the physiological sensors comprises a biomedical microelectromechanical (MEMs) system configured to perform sample preconditioning and output preconditioned sample data.

20. The system as defined in claim 18, wherein at least one of the physiological sensors is configured to wirelessly communicate over a 2360-2400 MHz radio frequency band and/or a 30-37.5 MHz radio frequency band.

21. The system as defined in claim 18, wherein at least one of the physiological sensors is assigned a unique Internet Protocol address.

22. The system as defined in claim 18, wherein at least one of the physiological sensors comprises an implanted sensor or a sensor surface-mounted on a body of the user in a fixed position.

23. The system as defined in claim 18, wherein at least one of the physiological sensors is worn using a wrist band.

24. The system as defined in claim 18, wherein at least one of the physiological sensors comprises a processing device, a battery, and a wireless transceiver.

25. The system as defined in claim 18, wherein the system is configured to:

dynamically generate a data interface to collect a plurality of types of health status information, demographic information, and clinical information, wherein the interface comprises a human-readable interface and/or a machine-readable interface;

collect the plurality of types of health status information, demographic information, and clinical information using the data interface;

identify potentially significant predictors of future health states based at least in part on information collected using the data interface;

generate the first biological event predictive model using the identified potentially significant predictors of future health states.

26. The system as defined in claim 18, wherein the system is configured to:

generate treatment recommendations based at least in part on generated predictor results obtained from executing the first biological event predictive model.