Abstract: The hand which a user is using to hold an electronic device can be determined by analyzing data captured by one or more motion sensors on the device. The curvature to the motion can be indicative of handedness, and processing motion features using a classifier algorithm can enable the determination of handedness with a corresponding confidence. In some embodiments, motion data is collected over a monitoring window, and handedness values are accepted when the handedness value remains the same with at least a minimum confidence for at least a minimum number of window periods. A determination of handedness enables an operating system and/or applications executing on the device to adjust one or more operational or interface aspects in order to make it easier for the user to operate the device using the hand currently holding the device.

Abstract: An electronic device can be configured to enable a user to provide input via a tap of the device without the use of touch sensors (e.g., resistive, capacitive, ultrasonic or other acoustic, infrared or other optical, or piezoelectric touch technologies) and/or mechanical switches. Such a device can include other sensors, including inertial sensors (e.g., accelerometers, gyroscopes, or a combination thereof), microphones, proximity sensors, ambient light sensors, and/or cameras, among others, that can be used to capture respective sensor data. Feature values with respect to the respective sensor data can be extracted, and the feature values can be analyzed using machine learning to determine when the user has tapped on the electronic device. Detection of a single tap or multiple taps performed on the electronic device can be utilized to control the device.

Abstract: An electronic device can be configured to enable a user to provide input via a tap of the device without the use of touch sensors (e.g., resistive, capacitive, ultrasonic or other acoustic, infrared or other optical, or piezoelectric touch technologies) and/or mechanical switches. Such a device can include other sensors, including inertial sensors (e.g., accelerometers, gyroscopes, or a combination thereof), microphones, proximity sensors, ambient light sensors, and/or cameras, among others, that can be used to capture respective sensor data. Feature values with respect to the respective sensor data can be extracted, and the feature values can be analyzed using machine learning to determine when the user has tapped on the electronic device. Detection of a single tap or multiple taps performed on the electronic device can be utilized to control the device.