A computing device is described that uses motion data from motion sensors to detect gestures or user inputs, such as out-of-screen user inputs for mobile devices. In other words, the computing device detects gestures or user touch inputs at locations of the device that do not include a touch screen, such as anywhere on the surface of the housing or the case of the device. A tactile texture is applied to a housing of the computing device or a case that is coupled to the housing. The tactile texture causes the computing device to move in response to a user input applied to the tactile texture, such as when a user’s finger slides over the tactile texture. A motion sensor (e.g., an inertial measurement unit (IMU), accelerometer, gyroscope, etc.) generates motion data in response to detecting the motion of the computing device. The motion data is processed by an artificial neural network to infer attributes of the user input. In other words, the computing device applies a machine-learned model to the motion data (also referred to as sensor data or motion sensor data) to classify or label the various attributes, characteristics, or qualities of the input. In this way, the computing device utilizes machine learning and motion data to classify attributes of the user input or gesture utilizing motion sensors without the need for additional hardware, such as touch-sensitive devices and sensors.
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Huang, Michael Xuelin; Jensen, Scott; and Zhai, Shumin, "TACTILE TEXTURES FOR BACK OF SCREEN GESTURE DETECTION USING MOTION SENSOR DATA AND MACHINE LEARNING", Technical Disclosure Commons, (December 13, 2019)