Abstract
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. The techniques described enable a computing device to utilize a standard, existing motion sensor (e.g., an inertial measurement unit (IMU), accelerometer, gyroscope, etc.) to detect the user input and determine attributes of the user input. Motion data generated by the motion sensor (also referred to as a movement sensor) 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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Huang, Michael Xuelin and Zhai, Shumin, "DETECTING GESTURES UTILIZING MOTION SENSOR DATA AND MACHINE LEARNING", Technical Disclosure Commons, (July 22, 2019)
https://www.tdcommons.org/dpubs_series/2363