Abstract: Various implementations track hand motion to interpret a scroll movement. For example, an example process may include obtaining sensor data associated with a hand via one or more sensors in a physical environment. The process may further include determining positional data corresponding to three-dimensional (3D) positions of two points on the hand based on the sensor data. The process may further include determining whether a movement of the hand includes a wrist rotation associated with the hand rotating about the wrist, an arm motion associated with movement of the arm, or a combination of the wrist rotation and the arm motion. The process may further include determining the movement of the hand as a user interaction event based on the positional data and whether the movement of the hand includes the wrist rotation, the arm motion, or a combination of the wrist rotation and the arm motion.

Abstract: Processing gesture input includes obtaining hand tracking data for a first hand based on one or more camera frames, detecting a first input gesture by the first hand based on the hand tracking data, and determining whether the first hand is in an active state. An input action associated with the first gesture is initiated in accordance with a determination that the first hand is in the active state. If, while the hand is in an active state, a determination is made that the inactive criterion is satisfied, then the first hand is transitioned to an inactive state.

Abstract: Processing gesture input includes obtaining hand tracking data for a hand from one or more sensors, wherein the hand performs a pinch in the hand tracking data, determining a hand position and a hand orientation for the hand from the hand tracking data, applying a predefined offset from the hand position based on the hand orientation to determine a pinch centroid, and determining an input location based on the pinch centroid. The pinch centroid is used in an indirect motion mapping pipeline to determine characteristics of user input based on user motion.

Abstract: Various implementations disclosed herein include devices, systems, and methods that interpret user activity as user interactions with user interface (UI) elements positioned within a three-dimensional (3D) space such as an extended reality (XR) environment. Some implementations enable user interactions with virtual elements displayed in 3D environments that utilize alternative input modalities, e.g., XR environments that interpret user activity as either direct interactions or indirect interactions with virtual elements.

Abstract: Processing gesture input includes obtaining hand tracking data based on a set of camera frames, determining a hand pose based on the hand tracking data, and determining an intentionality classification for a gesture based on the hand pose. An input action corresponding to the gesture is enabled based on the hand pose and the intentionality classification. An occlusion classification is determined for the hand based on the hand pose and the input gesture can be determined based on the occlusion classification.

Abstract: Techniques for mapping a user input motion includes detecting an input motion by a user, determining an origin for an input motion in a user-centric spherical coordinate system, determining an arc length for the input motion based on the determined origin, mapping the arc length of the input motion to a 2D plane of a user input component, and presenting a movement of a user input component on the 2D plane in accordance with the mapping.

Abstract: Suppressing a hand gesture upon detecting peripheral events on a peripheral device includes determining a first hand pose for a first hand a second hand pose for a second hand in response to a detected peripheral device peripheral event, determining, based on the first hand pose and the second hand pose, at least one hand of the first hand and the second hand in a peripheral use mode, detecting an input gesture from a hand of the at least one hand determined to be in the peripheral use mode, and rejecting the input gesture by a user input pipeline in accordance with the determination that the hand is in the peripheral use mode. The presence of a peripheral device is confirmed by activating a computer vision system in response to determining that a peripheral use condition is satisfied.

Abstract: A multistep finite control set model predictive control method for linear induction machines is provided, and the method belongs to a control technology field for linear induction machines. The method specifically includes the following steps: collecting a primary phase current of a linear induction machine in a three-phase coordinate system; solving a multistep reference voltage vector sequence by iteration according to a current value; keeping only two non-zero voltage vectors and one zero voltage vector that are closest to a reference voltage vector in each predictive step; and further eliminating a voltage vector sequence having a large cost function value through dynamic online comparison with a cost function value.

Abstract: The disclosure discloses a linear oscillatory machine control method based on an adaptive full-order displacement observer.

Abstract: The disclosure discloses a linear oscillatory machine control method based on an adaptive full-order displacement observer.

Abstract: A multistep finite control set model predictive control method for linear induction machines is provided, and the method belongs to a control technology field for linear induction machines. The method specifically includes the following steps: collecting a primary phase current of a linear induction machine in a three-phase coordinate system; solving a multistep reference voltage vector sequence by iteration according to a current value; keeping only two non-zero voltage vectors and one zero voltage vector that are closest to a reference voltage vector in each predictive step; and further eliminating a voltage vector sequence having a large cost function value through dynamic online comparison with a cost function value.