AUTOMATED ENVIRONMENT HAZARD DETECTION

• US 20170140631A1
• Filed: 09/16/2016
• Published: 05/18/2017
• Est. Priority Date: 09/17/2015
• Status: Active Grant

• Current Assignee: Luvozo PBC
• Original Assignee: Luvozo PBC
• Inventors: Pietrocola, David, Kessler, Terrance Jude, Charifa, Mohammed Samer, Ogunfemi, Babatunde O.
• Granted Patent: US 11,170,626 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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: G01C 21/206 specially adapted for indoor navigation
• CPC Class Codes: G01J 1/4204 with determination of ambient light solar light G01J2001/4266
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G08B 21/0476 Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data
• CPC Class Codes: G16H 40/63 for local operation

Abstract

Systems and techniques are provided in which one or more environmental sensors collect data about an environment. An environmental hazard assessment module collects and analyzes data obtained by the environmental sensors to automatically identify, categorize, and/or rate the severity of potential environmental hazards. The hazards are provided to a user for a particular region of the environment or for a larger environment that includes multiple regions.

Claims

1. A system comprising:

a plurality of environmental sensors, each environmental sensor configured to measure a physical attribute of an environment in which the system is disposed;

a processing system configured to receive environmental data from the plurality of environmental sensors and, based upon the environmental data, identify at least one potential fall hazard in the environment and a degree of risk associated with the fall hazard;

a reporting component configured to provide an indication of the identified fall hazard and the degree of risk associated with the fall hazard.

2. The system of claim 1, wherein the system comprises a portable computing device, the portable computing device comprising the plurality of environmental sensors, the processing system, and the reporting component.

3. The system of claim 2, wherein the reporting component comprises a display screen.

4. The system of claim 2, wherein the portable computing device is selected from the group consisting of:

a tablet, a smart phone, and a portable general-purpose computer.

5. The system of claim 1, wherein the plurality of environmental sensors comprises an RGB camera configured to capture image data of the environment.

6. The system of claim 5, wherein the processing system is configured to identify a floor transition, an item of clutter, or both based upon the image data.

7. The system of claim 1, wherein the plurality of environmental sensors comprises a topology sensor configured to collect topological data about the environment, and wherein the processing system is configured to identify a floor transition as a potential fall hazard based upon the topological data.

8. The system of claim 1, wherein the plurality of environmental sensors comprises an ambient light sensor, and wherein the processing system is configured to identify a low light condition as a potential fall hazard based upon ambient light data collected by the ambient light sensor.

9. The system of claim 1, wherein the processing system is configured to implement a neural network to identify the potential fall hazard based upon the environmental data, wherein the neural network is trained based upon historic environmental data.

10. The system of claim 1, comprising:

a portable computing device, wherein the portable computing device comprises the plurality of environmental sensors; and

a network communication interface, configured to provide the environmental data to the processing system.

11. A method comprising:

receiving, from each of a plurality of environmental sensors, environmental data describing an environment in which the plurality of sensors is disposed;

based upon the environmental data, automatically identifying at least one potential fall hazard in the environment;

based upon the environmental data, automatically determining a degree of risk associated with the fall hazard; and

automatically generating a report indicating the presence of the fall hazard and the degree of risk associated with the fall hazard.

12. The method of claim 11, wherein the plurality of environmental sensors are disposed within a portable computing device.

13. The method of claim 12, further comprising automatically displaying the report on a display screen of the portable computing device.

14. The method of claim 12, wherein the portable computing device is selected from the group consisting of:

a tablet, a smart phone, and a portable general-purpose computer.

15. The method of claim 11, wherein the plurality of environmental sensors comprises an RGB camera configured to capture image data of the environment.

16. The method of claim 15, wherein the step of identifying at least one potential fall hazard comprises identifying a floor transition, an item of clutter, or both based upon the image data.

17. The method of claim 11, wherein the plurality of environmental sensors comprises a topology sensor configured to collect topological data about the environment, and wherein the step of identifying at least one potential fall hazard comprises identifying a floor transition as the potential fall hazard based upon the topological data.

18. The method of claim 11, wherein the plurality of environmental sensors comprises an ambient light sensor, and wherein the step of identifying at least one potential fall hazard comprises identifying a low light condition as the potential fall hazard based upon ambient light data collected by the ambient light sensor.

19. The method of claim 11, wherein the step of automatically identifying the at least one potential fall hazard, the step of automatically determining the degree of risk, or both, is performed by an artificial neural network.

20. The method of claim 11, further comprising providing the environmental data to a remote computing platform, wherein the remote computing platform generates the report.