Machine-learning-based proximity detection using impedance
AssigneeQUALCOMM Incorporated
InventorsDavid Loweth WINSLOW, John BORO, Diego CALZOLARI, John FORRESTER
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Abstract
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. Impedance information for a wireless transmitter of a device is determined, and impedance change information is generated based on a difference between the impedance information and prior impedance information for the wireless transmitter. An off-body characteristic is generated based on processing the impedance change information using a trained machine learning model, where the off-body characteristic indicates a probability that the device was off-body when the impedance information was determined.
Claims30
A processor-implemented method for proximity detection using machine learning, comprising:
- determining impedance information for a wireless transmitter of a device;
- generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter;
- and
- generating an off-body characteristic based on processing the impedance change information using a trained machine learning model, wherein the off-body characteristic indicates a probability that the device was off-body when the impedance information was determined.
A processor-implemented method for training machine learning models to perform proximity detection, comprising:
- determining impedance information for a wireless transmitter of a device;
- generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter;
- generating an off-body characteristic based on processing the impedance change information using a machine learning model, wherein the off-body characteristic indicates a probability that the device was off-body when the impedance information was determined;
- and
- updating one or more parameters of the machine learning model based on comparing the off-body characteristic with a ground truth label associated with the impedance information.
A processing system, comprising:
- a memory comprising computer-executable instructions;
- and
- one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation comprising:
- determining impedance information for a wireless transmitter of the processing system;
- generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter;
- and
- generating an off-body characteristic based on processing the impedance change information using a trained machine learning model, wherein the off-body characteristic indicates a probability that the processing system was off-body when the impedance information was determined.
A processing system, comprising:
- means for determining impedance information for a wireless transmitter of a processing system;
- means for generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter;
- and
- means for generating an off-body characteristic based on processing the impedance change information using a trained machine learning model, wherein the off-body characteristic indicates a probability that the processing system was off-body when the impedance information was determined.
Dependencies are inferred from the claim text.