Surgical robotic system having anthropometry-based user console

Patent metadata

Field	Value
Publication number	US-11666156-B2
Application number	US-202217898146-A
Country	US
Kind code	B2
Filing date	Aug 29, 2022
Priority date	Jul 13, 2018
Publication date	Jun 6, 2023
Grant date	Jun 6, 2023

Abstract

Surgical robotic systems including a user console for controlling a robotic arm or a surgical robotic tool are described. The user console includes components designed to automatically adapt to anthropometric characteristics of a user. A processor of the surgical robotic system is configured to receive anthropometric inputs corresponding to the anthropometric characteristics and to generate an initial console configuration of the user console based on the inputs using a machine learning model. Actuators automatically adjust a seat, a display, or one or more pedals of the user console to the initial console configuration. The initial console configuration establishes a comfortable relative position between the user and the console components. Other embodiments are described and claimed.

First claim

What is claimed is:

1. A method of configuring a user console, the method comprising:
receiving a plurality of anthropometric parameters of a user;
processing the plurality of anthropometric parameters of the user using a machine learning model, wherein the machine learning model correlates the plurality of anthropometric parameters to a plurality of extended anthropometric parameters, and correlates the plurality of extended anthropometric parameters to a plurality of console configuration parameters for one or more of a seat, a display, or one or more pedals of the user console; and
configuring the user console based on the plurality of console configuration parameters.

2. The method of claim 1 , wherein the plurality of anthropometric parameters includes at least two of a height, a gender, or a shoe size.

3. The method of claim 1 , wherein the plurality of extended anthropometric parameters includes one or more of a height, a gender, an upper arm length, a forearm length, an upper leg length, a lower leg length, a foot width, or a foot length.

4. The method of claim 1 , wherein the machine learning model includes a regularized regression model that recommends an initial configuration of the user console based on a correlation between physical attributes of the user and user console configuration preferences of the user.

5. The method of claim 1 , wherein the machine learning model comprises a predictive regression model simulating a correlation between the plurality of anthropometric parameters and the plurality of extended anthropometric parameters.

6. The method of claim 1 , wherein the machine learning model comprises a predictive regression model simulating a correlation between the plurality of extended anthropometric parameters and the plurality of console configuration parameters.

7. The method of claim 1 , wherein configuring the user console includes generating a set of actuation commands for adjusting the user console to an initial configuration, and further comprising actuating one or more actuators of the user console based on the set of actuation commands to adjust one or more of the seat, the display, or the one or more pedals to the initial configuration.

8. The method of claim 7 , wherein the initial configuration includes a pose, a position, or an angle of at least one of the seat, the display, or the one or more pedals.

9. A non-transitory computer-readable medium storing instructions which, when executed by one or more processors of a user console, cause the user console to perform a method comprising:
receiving a plurality of anthropometric parameters of a user;
processing the plurality of anthropometric parameters of the user using a machine learning model, wherein the machine learning model correlates the plurality of anthropometric parameters to a plurality of extended anthropometric parameters, and correlates the plurality of extended anthropometric parameters to a plurality of console configuration parameters for one or more of a seat, a display, or one or more pedals of the user console; and
configuring the user console based on the plurality of console configuration parameters.

10. The non-transitory computer-readable medium of claim 9 , wherein the plurality of anthropometric parameters includes at least two of a height, a gender, or a shoe size.

11. The non-transitory computer-readable medium of claim 9 , wherein the plurality of extended anthropometric parameters includes one or more of a height, a gender, an upper arm length, a forearm length, an upper leg length, a lower leg length, a foot width, or a foot length.

12. The non-transitory computer-readable medium of claim 9 , wherein the machine learning model includes a regularized regression model that recommends an initial configuration of the user console based on a correlation between physical attributes of the user and user console configuration preferences of the user.

13. The non-transitory computer-readable medium of claim 9 , wherein configuring the user console includes generating a set of actuation commands for adjusting the user console to an initial configuration, and wherein the method further comprises actuating one or more actuators of the user console based on the set of actuation commands to adjust one or more of the seat, the display, or the one or more pedals to the initial configuration.

14. The non-transitory computer-readable medium of claim 13 , wherein the initial configuration includes a pose, a position, or an angle of at least one of the seat, the display, or the one or more pedals.

15. A user console, comprising:
a seat;
a display;
one or more pedals; and
one or more processors configured to:
receive a plurality of anthropometric parameters of a user;
processing the plurality of anthropometric parameters of the user using a machine learning model, wherein the machine learning model correlates the plurality of anthropometric parameters to a plurality of extended anthropometric parameters, and correlates the plurality of extended anthropometric parameters to a plurality of console configuration parameters for one or more of a seat, a display, or one or more pedals of the user console; and
configure the user console based on the plurality of console configuration parameters.

16. The user console of claim 15 , wherein the plurality of anthropometric parameters includes at least two of a height, a gender, or a shoe size.

17. The user console of claim 15 , wherein the plurality of extended anthropometric parameters includes one or more of a height, a gender, an upper arm length, a forearm length, an upper leg length, a lower leg length, a foot width, or a foot length.

18. The user console of claim 15 , wherein the machine learning model includes a regularized regression model that recommends an initial configuration of the user console based on a correlation between physical attributes of the user and user console configuration preferences of the user.

19. The user console of claim 15 further comprising one or more actuators, wherein configuring the user console includes generating a set of actuation commands for adjusting the user console to an initial configuration, and wherein the one or more processors are further configured to actuate the one or more actuators based on the set of actuation commands to adjust one or more of the seat, the display, or the one or more pedals to the initial configuration.

20. The user console of claim 19 , wherein the initial configuration includes a pose, a position, or an angle of at least one of the seat, the display, or the one or more pedals.

Assignees

• Verb Surgical Inc

Inventors

• Hondori Hossein Mousavi
• Savall Joan
• Sani Hamid Reza
• Nobles Brent Michael

Classifications

• A47C1/02

  Reclining or easy chairs · CPC title

• G06N20/00

  Machine learning · CPC title

• B25J13/02

  Hand grip control means {(handles or pedals for crane control B66C13/56; for measuring the force applied to control members G01L5/22; hand-held casings for switching devices, e.g. joy-sticks H01H9/0214)} · CPC title

• A61B5/1122

  of movement trajectories · CPC title

• B25J13/04

  Foot-operated control means · CPC title