PROVIDING PERSONALIZED PATIENT CARE BASED ON ELECTRONIC HEALTH RECORD ASSOCIATED WITH A USER

• US 20160250751A1
• Filed: 02/26/2015
• Published: 09/01/2016
• Est. Priority Date: 02/26/2015
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Inventors: Martinson, Eric, Sisbot, Emrah Akin, Yalla, Veeraganesh, Oguchi, Kentaro, Nakano, Yusuke
• Granted Patent: US 9,694,496 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B25J 11/009 Nursing, e.g. carrying sick persons, pushing wheelchairs, distributing drugs
• CPC Class Codes: B25J 9/163 learning, adaptive, model based, rule based expert control
• CPC Class Codes: B25J 9/1674 characterised by safety, monitoring, diagnostic
• CPC Class Codes: G05D 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y10S 901/01 Mobile robot
• CPC Class Codes: Y10S 901/03 Teaching system
• CPC Class Codes: Y10S 901/47 Optical

Abstract

The disclosure includes methods for determining a current location for a user in an environment; detecting obstacles within the environment; estimating one or more physical capabilities of the user based on an EHR associated with the user; generating, with a processor-based device that is programmed to perform the generating, instructions for a robot to perform a task based on the obstacles within the environment and one or more physical capabilities of the user; and instructing the robot to perform the task.

Claims

1. A method comprising:

determining a current location for a user in an environment;

detecting obstacles within the environment;

estimating one or more physical capabilities of the user based on an electronic health record (EHR) associated with the user, wherein the EHR includes a description of a current medical condition of the user and a historical medical condition of the user;

generating, with a special purpose processor-based computing device that is programmed to perform the generating, instructions for a robot to perform a hand-over motion for moving an object from an original location to a position and rotation within the environment, wherein the instructions are generated based on the obstacles within the environment and the one or more physical capabilities of the user;

instructing the robot to perform the hand-over motion;

tracking a status of the user;

responsive to a change to the status of the user, generating modified instructions for the robot to perform the hand-over motion; and

updating the EHR to include the change to the status of the user.

2. A method comprising:

determining a current location for a user in an environment;

detecting obstacles within the environment;

estimating one or more physical capabilities of the user of the user based on an electronic health record (EHR) associated with the user;

generating, with a processor-based device that is programmed to perform the generating, instructions for a robot to perform a task based on the obstacles within the environment and the one or more physical capabilities of the user of the user; and

instructing the robot to perform the task.

3. The method of claim 2, wherein the task is a hand-over motion for moving an object from an original location to a position and rotation within the environment.

4. The method of claim 2, wherein estimating the one or more physical capabilities of the user is further based on at least one of a current medical condition of the user and a historical medical condition of the user.

5. The method of claim 4, wherein:

the task is a hand-over motion for moving an object from an original location to a position and rotation within the environment; and

the historical medical condition includes at least one of a range of motion for the user, reachability for the user, muscle tension for the user, comfort associated with the user, a preference associated with the user, a time for the user to pick up the object from the robot, a time for the user to make contact with the robot after the hand-over motion, and a time of completion of the task.

6. The method of claim 2, wherein estimating the one or more physical capabilities of the user is further based on user preferences associated with the user.

7. The method of claim 2, wherein the task is to carry an object for the user.

8. The method of claim 2, wherein the task is a hand-over motion and further comprising:

determining whether a closest hand that is closest to the robot is injured based on at least one of a range of motion for the closest hand and a reachability for the closest hand; and

responsive to the closest hand being injured, computing the hand-over motion for moving an object from an original location to a furthest hand that is furthest from the robot.

9. The method of claim 2, wherein the task is a path for the user to travel, wherein estimating the path for the robot to travel is based on at least one of a speed of the user, a relative position to the user, a difficulty of the path, and an amount of information the robot provides to the user for guidance, and further comprising:

instructing the robot to guide the user by traveling along the path.

10. The method of claim 9, further comprising determining a condition of the user based on the EHR and wherein estimating the path includes estimating a distance between the robot and the user based on the condition of the user.

11. The method of claim 2, wherein estimating the one or more physical capabilities of the user based on the EHR further comprises:

building an EHR query;

querying a database for the EHR;

receiving the EHR; and

generating a simulation based on the EHR.

12. The method of claim 11, wherein generating the simulation includes generating classifiers and training sets that are used to identify a condition associated with the user.

13. The method of claim 12, further comprising:

tracking a status of the user;

responsive to a change to the status of the user, generating modified instructions for the robot to perform the task; and

updating the EHR to include a change to the status of the user.

14. A non-transitory computer-readable medium having computer instructions stored thereon that are executable by a processing device to perform or control performance of steps comprising:

determining a current location for a user in an environment;

detecting obstacles within the environment;

estimating one or more physical capabilities of the user based on an electronic health record (EHR) associated with the user;

generating instructions for a robot to perform a task based on the obstacles within the environment and the one or more physical capabilities of the user; and

instructing the robot to perform the task.

15. The non-transitory computer-readable medium of claim 14, wherein:

the task is a hand-over motion for moving an object from an original location to a position and rotation within the environment; and

estimating the one or more physical capabilities of the user is further based on at least one of a current medical condition of the user and a historical medical condition of the user, the historical medical condition including at least one of a range of motion for the user, reachability for the user, muscle tension for the user, comfort associated with the user, a time for the robot to pick up the object with a hand, a time for the hand-over motion, and a time of completion of a task.

16. The non-transitory computer-readable medium of claim 15, wherein the task is to carry an object for the user.

17. The non-transitory computer-readable medium of claim 15, wherein the task is a hand-over motion, and the steps further comprising:

determining whether a closest hand that is closest to the robot is injured based on at least one of a range of motion for the closest hand and a reachability for the closest hand; and

responsive to the closest hand being injured, computing the hand-over motion for moving an object from an original location to a furthest hand that is furthest from the robot.

18. The non-transitory computer-readable medium of claim 15, wherein the task is a path for the user to travel, wherein estimating the path for the robot to travel is based on at least one of a speed of the user, a relative position to the user, a difficulty of the path, and an amount of information the robot provides to the user for guidance, and the steps further comprising:

instructing the robot to guide the user by traveling along the path.

19. The non-transitory computer-readable medium of claim 18, the steps further comprising determining a condition of the user based on the EHR and wherein estimating the path includes estimating a distance between the robot and the user based on the condition of the user.

20. The non-transitory computer-readable medium of claim 15, wherein estimating the one or more physical capabilities of the user based on the EHR further comprises:

building an EHR query;

querying a database for the EHR;

receiving the EHR; and

generating a simulation based on the EHR.

21. A method for improving the behavior of a robot, the method comprising:

building an electronic health record query (EHR query) including an identifier of a user;

querying a database for electronic health record data (EHR data) associated with the identifier, wherein the EHR data describes one or more physical capabilities of the user;

generating simulation data for the user based on the EHR data, wherein the simulation data describes a simulation including expected behavior of the robot when executing an initial program configured to control the behavior of the robot; and

determining a customized program for the robot based on the simulation data and the initial program, wherein the customized program controls the behavior of the robot based on the one or more physical capabilities of the user.

22. The method of claim 21, further comprising:

executing the customized program; and

determined performance feedback data based on the behavior of the robot in relation to the user.

23. The method of claim 22, further comprising modifying the customized program based on the performance feedback data.

24. The method of claim 22, further comprising determining a new customized program for the user based on the simulation data, the customized program and the performance feedback data.

25. The method of claim 22, further comprising:

adding the performance feedback data to the EHR data; and

generating new simulation data for the user based on the performance feedback data included in the EHR data.

26. A method for customizing robot perception of a user, the method comprising:

querying a database for electronic health record data (EHR data) that describes one or more electronic health records associated with a user;

determining one or more physical capabilities of the user based on the EHR data;

selecting a set of images as training data based on the conditions or limitations of the user;

building a user-specific visual classifier for the user based on the EHR data associated and the training data; and

customizing the robot perception of the user based on the user-specific visual classifier for the user.

27. The method of claim 26, wherein the set of images are selected to correspond to the one or more physical capabilities of the user.

28. The method of claim 26, wherein the user-specific visual classifier is configured for use by the robot when providing a service to the user associated with the one or more electronic health records described by the EHR data.

29. The method of claim 26, wherein the user-specific visual classifier is built based on the conditions or limitations of the user as described by the EHR data so that the robot perception is customized for the conditions or limitations of the user.

30. A method for customizing robot navigation for a user, the method comprising:

determining one or more physical capabilities of the user based on electronic health record data (EHR data) that describes one or more electronic health records associated with a user;

determining modifications for a program that controls the behavior of the robot based on the EHR data; and

customizing the program for the robot based on the modifications so that the robot behavior is customized consistent with the one or more physical capabilities of the user.

31. The method of claim 30, wherein the EHR data indicates that the physical capabilities of the user includes low vision and the program for the robot is customized so that the robot navigates at a distance closer to the user than in a normal scenario and decelerates faster than in a normal scenario.

32. The method of claim 30, wherein the EHR data indicates that the physical capabilities of the user includes deafness in a first ear and the program for the robot is customized so that the robot navigates so that the robot is oriented closer to a second ear which is not deaf.

33. A method for a robot to assist a target user, the method comprising:

determining the target user;

retrieving electronic health record data (EHR data) associated with the target user and describing one or more physical capabilities of the target user;

detecting one or more obstacles within an environment including the target user;

customizing, with a processor-based device that is programmed to perform the customizing, instructions for the robot to perform one or more tasks for the target user based on the obstacles within the environment and the one or more physical capabilities of the target user; and

performing the one or more tasks for the target user based on the customized instructions.

34. The method of claim 33, wherein the task is a hand-over motion for moving an object from an original location to a position and rotation within the environment.

35. The method of claim 33, further comprising estimating the one or more physical capabilities of the user based on at least one of a current medical condition of the user and a historical medical condition of the user which is described by the EHR data.

36. The method of claim 33, wherein the task includes locating the user in the environment.

37. The method of claim 33, wherein the task includes carrying an object to the user.