Abstract: An electric field (e-field) touchscreen is described. A continuous stream of digital signal data that represents e-field signal deviations is received from multiple receive electrodes. The stream of digital signal data is processed using a machine learning model to determine a touch event and a location on a display screen of the touchscreen. The touch event is processed. The e-field touchscreen may determine whether a non-normal event may be occurring causing noise in the digital signal data. If so, the received stream of digital signal data is processed through a low-pass filter and processed through an absolute value average baseline filter. A difference between the filtered data is determined to generate a filtered stream of digital signal data and is processed using the machine learning model determine a touch event and a location on a display screen of the touch event. The touch event is processed.

Abstract: An electric field (e-field) touchscreen is described. A continuous stream of digital signal data that represents e-field signal deviations is received from multiple receive electrodes. The stream of digital signal data is processed using a machine learning model to determine a touch event and a location on a display screen of the touchscreen. The touch event is processed. The e-field touchscreen may determine whether a non-normal event may be occurring causing noise in the digital signal data. If so, the received stream of digital signal data is processed through a low-pass filter and processed through an absolute value average baseline filter. A difference between the filtered data is determined to generate a filtered stream of digital signal data and is processed using the machine learning model determine a touch event and a location on a display screen of the touch event. The touch event is processed.

Abstract: An electric field (e-field) touchscreen is described. A continuous stream of digital signal data that represents e-field signal deviations is received from multiple receive electrodes. The stream of digital signal data is processed using a machine learning model to determine a touch event and a location on a display screen of the touchscreen. The touch event is processed. The e-field touchscreen may determine whether a non-normal event may be occurring causing noise in the digital signal data. If so, the received stream of digital signal data is processed through a low-pass filter and processed through an absolute value average baseline filter. A difference between the filtered data is determined to generate a filtered stream of digital signal data and is processed using the machine learning model determine a touch event and a location on a display screen of the touch event. The touch event is processed.

Abstract: An electric field (e-field) touchscreen is described. A continuous stream of digital signal data that represents e-field signal deviations is received from multiple receive electrodes. The stream of digital signal data is processed using a machine learning model to determine a touch event and a location on a display screen of the touchscreen. The touch event is processed. The e-field touchscreen may determine whether a non-normal event may be occurring causing noise in the digital signal data. If so, the received stream of digital signal data is processed through a low-pass filter and processed through an absolute value average baseline filter. A difference between the filtered data is determined to generate a filtered stream of digital signal data and is processed using the machine learning model determine a touch event and a location on a display screen of the touch event. The touch event is processed.