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1. WO2020117533 - AUGMENTING THE FUNCTIONALITY OF NON-DIGITAL OBJECTS USING A DIGITAL GLOVE

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A computer-implemented method, comprising:

receiving sensor data generated by sensors in a digital glove while the digital glove is utilized to manipulate a non-digital object;

running a machine learning model to generate virtual user input device data based on the sensor data generated by the sensors in the digital glove while the digital glove is utilized to manipulate the non-digital object, the machine learning model being trained on sensor data generated by the sensors in the digital glove while the digital glove is utilized to manipulate an object; and

controlling a host computer using the virtual user input device data.

2. The computer-implemented method of claim 1, wherein the sensor data generated by the digital glove comprises pressure data generated by one or more pressure sensors of the digital glove.

3. The computer-implemented method of claim 1, wherein the sensor data generated by the digital glove comprises flex data generated by one or more flex sensors of the digital glove.

4. The computer-implemented method of claim 1, wherein the sensor data generated by the digital glove comprises inertial measurement unit (EMU) data generated by an EMU of the digital glove.

5. The computer-implemented method of claim 1 , wherein the obj ect comprises a user input device, and wherein the user input device comprises a digital dial.

6. A computing device, comprising:

a processor; and

a memory storing instructions executable by the processor to:

obtain sensor data from a digital glove coupled to the computing device, wherein the digital glove comprises a plurality of fingers, each of the plurality of fingers comprising a plurality of sensors configured to generate the sensor data, and wherein the sensor data is obtained from the sensors in the digital glove while the digital glove is utilized to manipulate a non-digital object;

execute a machine learning model to generate virtual user input device data based on the sensor data obtained from the sensors in the digital glove while the digital glove is utilized to manipulate the non-digital object; and

control the computing device using the virtual user input device data.

7. The computing device of claim 6, wherein the sensor data generated by the

digital glove comprises pressure data generated by one or more pressure sensors of the digital glove, flex data generated by one or more flex sensors of the digital glove, or inertial measurement unit (IMU) data generated by an IMU of the digital glove.

8. The computing device of claim 7, wherein the memory stores further instructions executable by the processor to:

determine if pressure data generated by the one or more pressure sensors indicates pressure exerted at one of the fingers in excess of a threshold value; and

initiate an operation at the computing device responsive to determining that the pressure data generated by the one or more pressure sensors indicates pressure exerted by one of the fingers in excess of a threshold value.

9. The computing device of claim 8, wherein the digital glove further comprises one or more haptic devices in the plurality of fingers, and wherein the memory stores further instructions executable by the processor to:

initiate feedback by the one or more haptic devices based upon the sensor data obtained from the sensors.

10. The computing device of claim 6, wherein the machine learning model is trained on sensor data generated by the sensors in the digital glove and data generated by a digital dial while the digital glove is utilized to manipulate the digital dial.

11. A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a processor, cause the processor to: obtain sensor data from a digital glove coupled to a computing device, wherein the digital glove comprises a plurality of fingers, each of the plurality of fingers comprising a plurality of sensors configured to generate the sensor data, and wherein the sensor data is obtained from the sensors in the digital glove while the digital glove is utilized to manipulate a non-digital object;

execute a machine learning model to generate virtual user input device data based on the sensor data obtained from the sensors in the digital glove while the digital glove is utilized to manipulate the non-digital object;

control the computing device using the virtual user input device data.

12. The computer-readable storage medium of claim 11, wherein the sensor data generated by the digital glove comprises pressure data generated by one or more pressure sensors of the digital glove, flex data generated by one or more flex sensors of the digital glove, or inertial measurement unit (IMU) data generated by an IMU of the digital glove.

13. The computer-readable storage medium of claim 12, having further

computer-executable instructions stored thereupon to:

determine if pressure data generated by the one or more pressure sensors indicates pressure exerted at one of the fingers of the digital glove in excess of a threshold value; and initiate a selection operation at the computing device responsive to determining that the pressure data generated by the one or more pressure sensors indicates pressure exerted by one of the fingers in excess of a threshold value.

14. The computer-readable storage medium of claim 13, wherein the machine learning model is trained on sensor data generated by the sensors in the digital glove and data generated by a user input device while the digital glove is utilized to manipulate wherein the user input device.

15. The computer-readable storage medium of claim 14, wherein the object comprises a user input device, and wherein the non-digital object comprises a cup or a non digital writing implement.