Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Touch sensor panels/screens can include a first region having a plurality of touch electrodes and a second region without touch electrodes. In some examples, to improve touch sensing performance, a first algorithm or a second algorithm is applied to determine whether an object corresponding to the touch patch is in contact with the touch screen. Whether to apply the first algorithm or the second algorithm is optionally dependent on the location of the touch patch.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.

Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.

Abstract: Computing devices and methods are used to detect and compensate for the presence of a cover layer on a touch input device. A computing device includes a processing device, a touch input device in electronic communication with the processing device, and a memory device in electronic communication with the processing device and having electronic instructions encoded thereon. The electronic instructions, when executed by the processing device, cause the processor to receive a first signal obtained from the touch input device over a first duration of time, the first signal including a first signal pattern, receive a second signal obtained from the touch input device over a second duration of time separate from the first duration of time, the second signal including a second signal pattern, determine a difference between the first signal pattern and the second signal pattern, and adjust a touch input detection setting based on the difference.

Abstract: Computing devices and methods are used to detect and compensate for the presence of a cover layer on a touch input device. A computing device includes a processing device, a touch input device in electronic communication with the processing device, and a memory device in electronic communication with the processing device and having electronic instructions encoded thereon. The electronic instructions, when executed by the processing device, cause the processor to receive a first signal obtained from the touch input device over a first duration of time, the first signal including a first signal pattern, receive a second signal obtained from the touch input device over a second duration of time separate from the first duration of time, the second signal including a second signal pattern, determine a difference between the first signal pattern and the second signal pattern, and adjust a touch input detection setting based on the difference.

Abstract: Computing devices and methods are used to detect and compensate for the presence of a cover layer on a touch input device. A computing device includes a processing device, a touch input device in electronic communication with the processing device, and a memory device in electronic communication with the processing device and having electronic instructions encoded thereon. The electronic instructions, when executed by the processing device, cause the processor to receive a first signal obtained from the touch input device over a first duration of time, the first signal including a first signal pattern, receive a second signal obtained from the touch input device over a second duration of time separate from the first duration of time, the second signal including a second signal pattern, determine a difference between the first signal pattern and the second signal pattern, and adjust a touch input detection setting based on the difference.

Abstract: A method and system for assessing an electrocardiogram (ECG) signal quality are disclosed. In a first aspect, the method comprises determining a Kurtosis calculation of the ECG signal and determining whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to determine a Kurtosis calculation of the ECG signal and to determine whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality.

Abstract: A method and system for contextual heart rate monitoring are disclosed. In a first aspect, the method comprises calculating a heart rate using a detected ECG signal and detecting an activity level. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to calculate a heart rate using a detected ECG signal and to detect an activity level.

Abstract: Cross-coupling correction techniques on a touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a touch image and perform cross-coupling correction to mitigate cross-talk due to routing traces of the touch sensor panel. Mitigating cross-talk can improve touch sensing accuracy, reduce jitter, and/or reduce false positive touch detection.

Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Embodiments are disclosed for a machine learning (ML) gesture recognition with a framework for adding user-customized gestures. In an embodiment, a method comprises: receiving sensor data indicative of a gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn on a limb of the user; generating a current encoding of features extracted from the sensor data using a machine learning model with the features as input; generating similarity metrics between the current encoding and each encoding in a set of previously generated encodings for gestures; generating similarity scores based on the similarity metrics; predicting the gesture made by the user based on the similarity scores; and performing an action on the wearable device or other device based on the predicted gesture.

Abstract: A method and system for low-distortion denoising of an ECG signal are disclosed. The method comprises determining at least one beat of the ECG signal for denoising using a beat selection logic and denoising the at least one beat using at least one ensemble averaging filter. The system includes a sensor to detect the ECG signal, a processor coupled to the sensor, wherein the processor includes a beat selection logic unit, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine at least one beat of the ECG signal for denoising using a beat selection logic and to denoise the at least one beat using at least one ensemble averaging filter.

Abstract: Finger detection and separation techniques on a multi-touch touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, a machine learning model can be used to process an input patch to disambiguate whether the input patch corresponds to one contact or two contacts. In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a sub-image including an input patch as an input, and can output a number of contacts. In some examples, the neural network can output one or more sub-image masks representing the one or more contacts.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface.

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface.

Abstract: A method and wireless sensor device for determining step count is disclosed. In one aspect, a method includes receiving sensor data. The method includes detecting if there is activity of the body based on the sensor data. If there is activity, the method also includes classifying the activity and determining step count based on classification of the activity.

Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.

Abstract: A method and system for assessing an electrocardiogram (ECG) signal quality are disclosed. In a first aspect, the method comprises determining a Kurtosis calculation of the ECG signal and determining whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to determine a Kurtosis calculation of the ECG signal and to determine whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality.