Abstract: One embodiment may take the form of a method of operating a computing device to provide presence based functionality. The method may include operating the computing device in a reduced power state and collecting a first set of data from a first sensor. Based on the first set of data, the computing device determines if an object is within a threshold distance of the computing device and, if the object is within the threshold distance, the device activates a secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If the object is a person, the device determines a position of the person relative to the computing device and executes a change of state in the computing device based on the position of the person relative to the computing device. If the object is not a person, the computing device remains in a reduced power state.

Abstract: One embodiment may take the form of a method of operating a computing device in a reduced power state and collecting a first set of data from at least one sensor. Based on the first set of data, the computing device determines a probability that an object is within a threshold distance of the computing device and, if so, the device activates at least one secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If it is a person, a position of the person relative to the computing device is determined and the computing device changes its state based on the position of the person. If the object is not a person, the computing device remains in a reduced power state.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: One embodiment may take the form of a method of operating a computing device to provide presence based functionality. The method may include operating the computing device in a reduced power state and collecting a first set of data from a first sensor. Based on the first set of data, the computing device determines if an object is within a threshold distance of the computing device and, if the object is within the threshold distance, the device activates a secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If the object is a person, the device determines a position of the person relative to the computing device and executes a change of state in the computing device based on the position of the person relative to the computing device. If the object is not a person, the computing device remains in a reduced power state.

Abstract: An electronic device that includes a proximity sensor may be provided. The proximity sensor may be a time-of-flight-based proximity sensor that is capable of separately outputting near-field measurements and far-field measurements. The near-field and far-field measurements may be placed in separate bins according to their time-of-flight values. The discrimination between near-field and far-field results may allow the electronic device to filter out false positive events where the presence of smudge or other surface contaminants can otherwise produce skewed readings and also to filter out false negative events where the presence of a user with dark hair or skin can otherwise produce misleading sensor results.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: An electronic device may be provided with electronic components such as a touch screen display. The touch screen display may be controlled based on information from a proximity sensor. The proximity sensor may have a light source that emits infrared light and a light detector that detects reflected infrared light. When the electronic device is in the vicinity of a user's head, the proximity sensor may produce data indicative of the presence of the user's head. Variations in proximity sensor output due to user hair color and smudges on the proximity sensor can be accommodated by using a dynamically adjustable proximity sensor data threshold in processing proximity sensor data. Proximity sensor data may be analyzed in real time to detect signal strength fluctuations that are indicative of the presence of low-reflectively dark hair on the user's head. Threshold adjustments may be based on the presence of dark hair.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.

Abstract: Methods, program products, and systems of motion-based device operations are described. A mobile device can coordinate operations of a motion sensor and a proximity sensor. The mobile device can determine a gesture event using the motion sensor. The mobile device can determine a proximity event using the proximity sensor. The mobile device can use the gesture event and proximity event to confirm one another, and determine that the mobile device has moved in proximity to a target object following a specified gesture. Upon confirmation, the mobile device can perform a specified task.

Abstract: Negative pixel compensation in a touch sensor panel is disclosed. A method can compensate for a negative pixel effect in touch signal outputs due to poor grounding of an object touching the panel. To do so, the method can include determining at least one bound for a negative pixel compensation factor based on touch signal values, estimating the compensation factor within the determined bound based on the touch signal values that are negative, where the negative values indicate the presence of the negative pixel effect, and applying the estimated compensation factor to the touch signal outputs to compensate the touch signal values for the negative pixel effect.

Abstract: An electronic device may be provided with electronic components such as a touch screen display. The touch screen display may be controlled based on information from a proximity sensor. The proximity sensor may have a light source that emits infrared light and a light detector that detects reflected infrared light. When the electronic device is in the vicinity of a user's head, the proximity sensor may produce data indicative of the presence of the user's head. Variations in proximity sensor output due to user hair color and smudges on the proximity sensor can be accommodated by using a dynamically adjustable proximity sensor data threshold in processing proximity sensor data. Proximity sensor data may be analyzed in real time to detect signal strength fluctuations that are indicative of the presence of low-reflectively dark hair on the user's head. Threshold adjustments may be based on the presence of dark hair.

Abstract: Negative pixel compensation in a touch sensor panel is disclosed. A method can compensate for a negative pixel effect in touch signal outputs due to poor grounding of an object touching the panel. To do so, the method can include determining at least one bound for a negative pixel compensation factor based on touch signal values, estimating the compensation factor within the determined bound based on the touch signal values that are negative, where the negative values indicate the presence of the negative pixel effect, and applying the estimated compensation factor to the touch signal outputs to compensate the touch signal values for the negative pixel effect.

Abstract: Sensor measurements are used to detect when a device incorporating the sensor is stationary. While the device is stationary, sensor measurements at a current device temperature are used to estimate model parameters. The model parameters can be used in a state estimator to provide an estimated attitude that can be provided to other applications. In some implementations, the estimated attitude can be used to mitigate interference in other sensor measurements.

Abstract: Negative pixel compensation in a touch sensor panel is disclosed. A method can compensate for a negative pixel effect in touch signal outputs due to poor grounding of an object touching the panel. To do so, the method can include determining at least one bound for a negative pixel compensation factor based on touch signal values, estimating the compensation factor within the determined bound based on the touch signal values that are negative, where the negative values indicate the presence of the negative pixel effect, and applying the estimated compensation factor to the touch signal outputs to compensate the touch signal values for the negative pixel effect.

Abstract: Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

Abstract: A device coupled with a magnetometer and an angular rate sensor can determine a heading of the device using magnetometer data. When the device receives a notification that the magnetometer data may be inaccurate, the device can determine the heading of the device using angular rate data. When the device determines that the magnetometer data are accurate, the device can resume determining the heading of the device using the magnetometer data.

Abstract: Methods, program products, and systems for gesture classification and recognition are disclosed. In general, in one aspect, a system can determine multiple motion patterns for a same user action (e.g., picking up a mobile device from a table) from empirical training data. The system can collect the training data from one or more mobile devices. The training data can include multiple series of motion sensor readings for a specified gesture. Each series of motion sensor readings can correspond to a particular way a user performs the gesture. Using clustering techniques, the system can extract one or more motion patterns from the training data. The system can send the motion patterns to mobile devices as prototypes for gesture recognition.