SLIDER AND GESTURE RECOGNITION USING CAPACITIVE SENSING

• US 20170131891A1
• Filed: 11/09/2015
• Published: 05/11/2017
• Est. Priority Date: 11/09/2015
• Status: Active Grant

• Current Assignee: Analog Devices, Inc.
• Original Assignee: Analog Devices, Inc.
• Inventors: NOVET, ISAAC CHASE
• Granted Patent: US 10,359,929 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/03547 Touch pads, in which fingers can move on a surface
• CPC Class Codes: G06F 3/03548 Sliders, in which the moving part moves in a plane
• CPC Class Codes: G06F 3/0416 Control or interface arrangements specially adapted for digitisers
• CPC Class Codes: G06F 3/044 by capacitive means
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text

Abstract

Conventional user interface for sensing gestures often require physical touching of a sensor pad, or an area filled with sensor pads. These conventional sensor pads take up precious real estate on a compact mobile device, interferes significantly with other components of the mobile device, complicates design, consumes power, and adds costs to the final product. With two or more small capacitive sensing electrodes placed on a mobile device (arbitrarily or with far less restrictions), capacitive sensing can provide a virtual sensor pad as a user interface. By implementing an algorithm which detects for three different conditions, the virtual sensor pad can be used effectively as a user interface modality to detect gestures such as a sliding gesture, drawing gesture, letters, writing, etc. The resulting virtual sensor pad can flexibly change to different desirable locations, and no longer suffers from the limitations of the conventional sensor pad.

Claims

1. A method for gesture recognition using capacitive sensing, the method comprising:

receiving capacitive sensor measurements made by two or more capacitive sensing electrodes over a period of time, said capacitive sensor measurements representative of disturbances to the respective electric fields generated by the two or more capacitive sensing electrodes;

determining, by a first classifier, crossover information and magnitude information from the capacitive sensor measurements;

determining, by a second classifier, derivative information from the capacitive sensor measurements;

determining, by a third classifier, path information from the capacitive sensor measurements; and

classifying a gesture based on respective outputs of the first, second, and third classifiers.

2. The method of claim 1, wherein the gesture comprises a sliding motion forming a line from a first point to a second point.

3. The method of claim 1, further comprising:

applying a coordinate transformation to the capacitive sensor measurements, wherein the coordinate transformation corresponds to a particular reference frame of the gesture.

4. The method of claim 3, further comprising:

changing or setting the coordinate transformation to change or set the reference frame of the gesture with respect to physical locations of the two or more capacitive sensing electrodes.

5. The method of claim 1, wherein determining crossover information and magnitude information by the first classifier comprises:

computing a ratio for a series of points in time, wherein a value of the ratio for a given point in time comprises a ratio between (1) a difference between two capacitive sensor measurements made by two different capacitive sensing electrodes at a given point in time and (2) a sum of the same two capacitive sensor measurements;

detecting a sign change in the ratio, wherein the sign change indicates a crossover point.

6. The method of claim 5, wherein determining crossover information and magnitude information by the first classifier further comprises:

determining (1) a first amount of magnitude change in capacitive sensor measurements made by a first one of the two different capacitive sensing electrodes over a period before a crossover point and a period after a crossover point, and (2) a second amount of magnitude change in capacitive sensor measurements made by a second one of the two different capacitive sensing electrodes over the same period; and

determining whether the first amount of magnitude change and/or the second amount of magnitude change exceeds a minimum delta threshold to qualify as a valid crossover point.

7. The method of claim 1, wherein determining derivative information by the second classifier comprises:

determining first capacitive sensor measurements from a first one of two different capacitive sensing electrodes measured over a period before and/or after a detected crossover point;

determining second capacitive sensor measurements from a second one of two different capacitive sensing electrodes measured over the same period;

determining derivatives of the first capacitive sensor measurements and derivatives of the second capacitive sensor measurements.

8. The method of claim 7, wherein determining derivative information by the second classifier further comprises:

determining whether a first average of the derivatives of the first capacitive sensor measurements and/or a second average of the derivatives of the second capacitive sensor measurements exceeds a minimum magnitude threshold.

9. The method of claim 7, wherein determining derivative information by the second classifier further comprises:

determining whether sign of the derivatives of the first capacitive sensor measurements and sign of the derivatives of the second capacitive sensor measurements are opposites of each other.

10. The method of claim 1, wherein determining path information by a third classifier comprises:

determining initial and end points of the gesture based on the capacitive sensor measurements from the two or more capacitive sensing electrodes;

determining estimated length of path travelled based on the initial and end points; and

comparing the estimated length of path travelled against an expected length of path travelled corresponding to the gesture.

11. The method of claim 1, wherein determining path information by a third classifier comprises:

determining initial and end points of the gesture based on the capacitive sensor measurements from the two or more capacitive sensing electrodes;

determining midpoint information along the path between the initial and end points of the gesture; and

comparing the midpoint information against an expected range corresponding to a predetermined midpoint of the gesture.

12. The method of claim 1, further comprising:

determining whether the outputs of the first, second, and third classifiers matches expected outputs corresponding to the gesture to classify whether the gesture was made by a user.

13. The method of claim 1, further comprising:

in response to classifying the gesture, triggering a user interface action corresponding to the gesture.

14. An apparatus for gesture recognition using capacitive sensing, the apparatus comprising:

two or more capacitive sensing electrodes for generating respective electric fields;

one or more processors configured for;

receiving capacitive sensor measurements made by two or more capacitive sensing electrodes over a period of time, said capacitive sensor measurements representative of disturbances to the respective electric fields generated by the two or more capacitive sensing electrodes;

determining, by a first classifier, crossover information and magnitude information from the capacitive sensor measurements;

determining, by a second classifier, derivative information from the capacitive sensor measurements;

determining, by a third classifier, path information from the capacitive sensor measurements; and

classifying a gesture based on respective outputs of the first, second, and third classifiers.

15. The apparatus of claim 14, further comprising a mechanical slider, wherein the two or more capacitive sensors are provided to supplement functionality of the mechanical slider or replace functionality of the mechanical slider if the mechanical slider fails.

16. One or more computer-readable non-transitory media comprising one or more instructions, for gesture recognition, that when executed on a processor configure the processor to perform one or more operations comprising:

receiving capacitive sensor measurements made by two or more capacitive sensing electrodes over a period of time, said capacitive sensor measurements representative of disturbances to the respective electric fields generated by the two or more capacitive sensing electrodes;

determining, by a first classifier, crossover information and magnitude information from the capacitive sensor measurements;

determining, by a second classifier, derivative information from the capacitive sensor measurements;

determining, by a third classifier, path information from the capacitive sensor measurements; and

classifying a gesture based on respective outputs of the first, second, and third classifiers.

17. The media of claim 16, wherein determining crossover information and magnitude information by the first classifier comprises:

computing a ratio for a series of points in time, wherein a value of the ratio for a given point in time comprises a ratio between (1) a difference between two capacitive sensor measurements made by two different capacitive sensing electrodes at a given point in time and (2) a sum of the same two capacitive sensor measurements;

detecting a sign change in the ratio, wherein the sign change indicates a crossover point.

18. The media of claim 16, wherein determining derivative information by the second classifier comprises:

determining first capacitive sensor measurements from a first one of two different capacitive sensing electrodes measured over a period before and/or after a detected crossover point;

determining second capacitive sensor measurements from a second one of two different capacitive sensing electrodes measured over the same period;

determining derivatives of the first capacitive sensor measurements and derivatives of the second capacitive sensor measurements; and

determining whether a first average of the derivatives of the first capacitive sensor measurements and/or a second average of the derivatives of the second capacitive sensor measurements exceeds a minimum magnitude threshold.

19. The media of claim 16, wherein determining path information by a third classifier comprises:

determining initial and end points of the gesture based on the capacitive sensor measurements from the two or more capacitive sensing electrodes;

determining estimated length of path travelled based on the initial and end points; and

comparing the estimated length of path travelled against an expected length of path travelled corresponding to the gesture.

20. The media of claim 16, wherein determining path information by a third classifier comprises:

determining initial and end points of the gesture based on the capacitive sensor measurements from the two or more capacitive sensing electrodes;

determining midpoint information along the path between the initial and end points of the gesture; and

comparing the midpoint information against an expected range corresponding to a predetermined midpoint of the gesture.