Route accessibility for users of mobility assistive technology

Patent metadata

Field	Value
Publication number	US-11561109-B2
Application number	US-201715651368-A
Country	US
Kind code	B2
Filing date	Jul 17, 2017
Priority date	Jul 17, 2017
Publication date	Jan 24, 2023
Grant date	Jan 24, 2023

Abstract

A database comprising data associated with one or more routes is maintained. The data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes. In response to receiving a query from a given computing device, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool are predicted. One or more routes for the given user to traverse are selected based at least in part on the predicted amounts of physical exertion. One or more contextual factors of the given user are estimated to at least one of optimize and prioritize the selected one or more routes for the given user based on analyzing user data.

First claim

What is claimed is:

1. A method comprising:
maintaining a database comprising data associated with one or more routes, wherein the data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes;
in response to receiving a query from a given computing device, predicting, based at least in part on the difficulty level data, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool;
responsive to the given user choosing a level of physical exertion, ranking the one or more routes based at least in part on the predicting of the one or more amounts of physical exertion;
selecting one or more routes for the given user to traverse based at least in part on the ranking of the one or more routes and identifying the given mobility assistive tool associated with the level of physical exertion chosen by the given user; and
estimating one or more contextual factors of the given user to at least one of coordinate and prioritize the selected one or more routes for the given user based on analyzing user data;
wherein the difficulty level data is a result of an analysis of one or more geographic characteristics of the one or more routes, the one or more contextual factors of the given user and the given mobility assistive tool;
wherein selecting the one or more routes further comprises:
prioritizing the ranking of the one or more selected routes;
selecting a user preferred interface for receiving the prioritized ranking of the one or more selected routes, comprising at least one of a graphical user interface of a user device, and a graphical user interface of a mobility assistive tool in the form of one or more of video and audio, wherein the given graphical user interface is an interactive graphical user interface displaying an accessibility map that is color coded to show the prioritized ranking of the one or more selected routes and one or more additional routes with difficulties based on the same and other mobility assistive tools;
providing the given user with a mobility assistive tool recommendation to utilize a different mobility assistive tool than the given mobility assistive tool based on the level of physical exertion chosen by the given user utilizing the color coded accessibility map; and
selecting the at least one route based on the prioritized ranking of the one or more routes and the mobility assistive tool recommendation;
wherein the steps are implemented by at least one processing device comprising a processor operatively coupled to memory.

2. The method of claim 1 , wherein the analysis of the one or more geographic characteristics of the one or more routes comprises, for each of the one or more routes, analyzing one or more of material composition, elevation, faults and weather conditions.

3. The method of claim 1 , wherein the analysis of the one or more geographic characteristics and the one or more routes, for each of the one or more routes, is performed at least in part by one or more neural networks implemented by the at least one processing device.

4. The method of claim 1 , wherein predicting of the one or more amounts of physical exertion for the given user further includes utilizing data associated with one or more goals of the given user.

5. The method of claim 1 , wherein predicting of the one or more amounts of physical exertion for the given user further includes utilizing crowdsourced data comprising physical exertion data for one or more other users.

6. The method of claim 1 , wherein identifying the given mobility assistive tool comprises utilizing one or more of a machine learning method and a signal processing method to analyze one or more device signal signatures while the given user is moving.

7. The method of claim 1 , further comprising receiving feedback from the given user traversing a given route, and updating the difficulty level data for the given route in real-time or near real-time based on the received feedback.

8. An article of manufacture comprising a non-transitory processor- readable storage medium for storing processor-readable program code which, when executed, causes a processor to perform the steps of:
maintaining a database comprising data associated with one or more routes, wherein the data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes;
in response to receiving a query from a given computing device, predicting, based at least in part on the difficulty level data, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool;
responsive to the given user choosing a level of physical exertion, ranking the one or more routes based at least in part on the predicting of the one or more amounts of physical exertion;
selecting one or more routes for the given user to traverse based at least in part on the ranking of the one or more routes and identifying the given mobility assistive tool associated with the level of physical exertion chosen by the given user; and
estimating one or more contextual factors of the given user to at least one of coordinate and prioritize the selected one or more routes for the given user based on analyzing user data;
wherein the difficulty level data is a result of an analysis of one or more geographic characteristics of the one or more routes, the one or more contextual factors of the given user and the given mobility assistive tool;
wherein selecting the one or more routes further comprises:
prioritizing the ranking of the one or more selected routes;
selecting a user preferred interface for receiving the prioritized ranking of the one or more selected routes, comprising at least one of a graphical user interface of a user device, and a graphical user interface of a mobility assistive tool in the form of one or more of video and audio, wherein the given graphical user interface is an interactive graphical user interface displaying an accessibility map that is color coded to show the prioritized ranking of the one or more selected routes and one or more additional routes with difficulties based on the same and other mobility assistive tools;
providing the given user with a mobility assistive tool recommendation to utilize a different mobility assistive tool than the given mobility assistive tool based on the level of physical exertion chosen by the given user utilizing the color coded accessibility map; and
selecting the at least one route based on the prioritized ranking of the one or more routes and the mobility assistive tool recommendation.

9. The article of manufacture of claim 8 , wherein the processor- readable program code which, when executed, further causes the processor to perform the step of:
receiving feedback from the given user traversing a given route, and updating the difficulty level data for the given route in real-time or near real-time based on the received feedback.

10. The article of manufacture of claim 8 , wherein predicting the one or more amounts of physical exertion for the given user further includes utilizing data associated with one or more goals of the given user.

11. The article of manufacture of claim 8 , wherein predicting the one or more amounts of physical exertion for the given user further includes utilizing crowdsourced data comprising physical exertion data for one or more other users.

12. The article of manufacture of claim 8 , wherein identifying the given

Assignees

• IBM

Inventors

• Pickover Clifford A
• Walcott Aisha
• Weldemariam Komminist

Classifications

• G06F16/29

  Geographical information databases · CPC title

• G01C21/3484 Primary

  Personalized, e.g. from learned user behaviour or user-defined profiles · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N5/003

  Physics · mapped topic

• G06N20/00

  Machine learning · CPC title