Abstract: This application relates to the field of electronic technologies, and provides an action recognition method and apparatus, a terminal device, and a motion monitoring system. Characteristic extraction and action recognition are performed based on motion data collected by data collection apparatuses; a gait characteristic, a swing gesture characteristic, and an image action characteristic of a user are recognized by using a plurality of pieces of motion data; and a type of a hitting action of a player is determined based on the gait characteristic, the swing gesture characteristic, and the image action characteristic. In this way, the hitting action of the player in a motion process can be accurately recognized. This is conducive to performing comprehensive analysis on a comprehensive sports capability of the player, and is more helpful to formulating a personalized training plan for the player.

Abstract: Embodiments herein describe a circuit including a passive intermodulation (PIM) model circuit configured to process first data to generate a PIM interference model output to be concatenated with second data, the second data including a first carrier frequency and a second carrier frequency, and the circuit further including a PIM model adapt circuit configured to receive frequency shifted captured data and frequency shifted PIM models to generate updated values to compensate for PIM interference after the PIM interference model output is concatenated with the second data.

Abstract: A cycling detection method, an electronic device, and a computer-readable storage medium are provided. The cycling detection method includes: obtaining an acceleration signal and an angular velocity signal collected by a wearable device of a user's foot, and analyzing a time-domain feature of the acceleration signal, a frequency-domain feature of the acceleration signal, a time-domain feature of the angular velocity signal, and/or a frequency-domain feature of the angular velocity signal; filtering the acceleration signal and the angular velocity signal to obtain a target acceleration signal and a target angular velocity signal of the user's cycling if it is determined, based on an analysis result, that a current status of the user is a cycling state; and determining a behavior feature of the user's cycling based on the target acceleration signal and the target angular velocity signal, where the behavior feature includes a cadence feature and a foot posture feature.

Abstract: The motion data monitoring method includes: collecting, by an electronic device, an angular velocity signal and an acceleration signal of a user; obtaining, by the electronic device, a waveform feature of the angular velocity signal based on the angular velocity signal, and obtaining a waveform feature of the acceleration signal based on the acceleration signal; determining, by the electronic device, a gait feature of the user according to the waveform feature of the angular velocity signal and the waveform feature of the acceleration signal, where the gait feature includes a duration of flight from off-ground of a foot of the user to touching of the ground; and determining, by the electronic device, motion data according to the gait feature, where the motion data includes a jump height.

Abstract: Embodiments of this application provide a target user locking method and an electronic device, and relate to the field of electronic devices. The method provided in this application may be applied to an artificial intelligence (artificial intelligence, AI) fitness scenario, and can ensure accuracy of locking a target user. The electronic device may recognize the target user and track the target user in a fitness process by using a method such as recognizing a user feature, recognizing the user with reference to data collected by a wearable device worn by the user, or recognizing a motion mode of the user.

Abstract: An adaptive meta-attack system for target trackers under autonomous driving scenarios including an initialization module, a meta-training iteration module, a meta-testing module, a perturbation generator, and an inference module. The initialization module includes a model initialization sub-module and a parameter initialization sub-module. The meta-training iteration module includes a video input sub-module, a training-validation model division sub-module, and a meta-training sub-module. The meta-testing module includes a performance validation and evaluation sub-module and a parameter output sub-module. An adaptive meta-attack method for a target tracker under autonomous driving scenarios applied in the adaptive meta-attack system is further provided.

Abstract: A method and an apparatus for setting a sports mode are provided. Two metal endpoints on an outer surface of an intelligent device are respectively used as an output end and a receive end of signals. A signal sent by the output end is compared with a signal received by the receive end to determine whether the intelligent device currently meets a preset condition. First sports characteristic data detected by a sports sensing apparatus is obtained to determine whether the first sports characteristic data matches preset sports characteristic data corresponding to a swimming mode. When two determining results are both “yes”, it can be determined that the intelligent device meets a setting condition of the swimming mode.

Abstract: A method includes, after receiving a first instruction, determining whether a wearable device has loaded map data of a first sport field, where the first instruction indicates that a user is playing a target sport, and the first sport field is a sport field in which the user plays the target sport; after it is determined that the wearable device has loaded the map data of the first sport field, obtaining motion data representing an action feature of the user in a case in which the user plays the target sport and positioning data representing an action location of the user in a case in which the user plays the target sport and a location of the wearable device; and displaying, based on the motion data, the positioning data, and the map data of the first sport field, statistical data about playing the target sport by the user.

Abstract: Embodiments herein describe a PIM correction circuit. In a base station, TX and RX RF changes, band pass filters, duplexers, and diplexers can have severe memory effects due to their sharp transition bandwidth from pass band to stop band. PIM interference, generated by the TX signals and reflected onto the RX RF chain will include these memory effects. These memory effects make PIM cancellation complex, requiring complicated computations and circuits. However, the embodiments herein use a PIM correction circuit that separates the memory effects of the TX and RX paths from the memory effects of PIM, thereby reducing PIM cancellation complexity and hardware implementation cost.

Abstract: Embodiments herein describe a PIM correction circuit. In a base station, TX and RX RF changes, band pass filters, duplexers, and diplexers can have severe memory effects due to their sharp transition bandwidth from pass band to stop band. PIM interference, generated by the TX signals and reflected onto the RX RF chain will include these memory effects. These memory effects make PIM cancellation complex, requiring complicated computations and circuits. However, the embodiments herein use a PIM correction circuit that separates the memory effects of the TX and RX paths from the memory effects of PIM, thereby reducing PIM cancellation complexity and hardware implementation cost.

Abstract: A digital predistortion (DPD) system includes an input configured to receive an input signal. In some examples, a first signal path configured to generate a first signal based on the input signal. In some examples, an error model provider configured to generate an error model signal modeled after a gate bias error voltage associated with the DPD system. In some examples, a first combiner configured to combine the first signal and the error model signal to generate a first intermediate signal, and the DPD system generates an output signal based at least on the first intermediate signal.

Abstract: A fall detection-based help-seeking method and an electronic device, to improve accuracy of fall detection performed by an electronic device and reduce a probability of mistakenly triggering automatic help-seeking of the electronic device. A solution includes: the electronic device includes a motion sensor and the motion sensor includes an acceleration sensor or a gyro sensor. The electronic device collects a first motion parameter of a user by using the motion sensor. The electronic device obtains a fall confidence of the first motion parameter if the first motion parameter matches a first preset fall parameter, where the fall confidence of the first motion parameter is used to represent a probability that the first motion parameter is a motion parameter collected when the user falls. The electronic device sends help-seeking information if the fall confidence of the first motion parameter is greater than a preset confidence threshold.

Abstract: An electronic device may learn a specific somatosensory action completed by a user, and also record a specific remote control key selected by the user, and then the electronic device may associate the action with the key. When recognizing that the user completes the action, the electronic device may simulate a control signal generated by the key, to control a game operation. Accordingly, the user can simply and quickly transform a conventional game into a somatosensory game. In this process, a game manufacturer does not need to perform additional customization work, and the user does not need to use an additional somatosensory device.

Abstract: A method for preventing hand gesture misrecognition is provided. In the method for preventing hand gesture misrecognition, whether a user is exercising is recognized by obtaining motion data of the user in real time, to choose whether to disable a hand gesture recognition function. If the object user is exercising, the electronic device does not perform hand gesture recognition; or if the object user is not exercising, the electronic device performs hand gesture recognition. In addition, in the method, a trigger threshold for hand gesture recognition may further be adaptively adjusted based on a deflection angle between a human body plane of the user and a plane on which the electronic device is located.

Abstract: A swing action detection method is applied to a wearable device including one or more motion sensors and a sound collector. In the method, the wearable device collects a first sound signal using the sound collector to determine whether a user strikes a ball. After a strike action is determined, an exercise parameter of the user is obtained with reference to first exercise data collected by the wearable device using the one or more motion sensors to monitor the strike action of the user, and effectively assist the user in improving a strike rhythm and strike stability.

Abstract: A cycling detection method, an electronic device, and a computer-readable storage medium are provided. The cycling detection method includes: obtaining an acceleration signal and an angular velocity signal collected by a wearable device of a user's foot, and analyzing a time-domain feature of the acceleration signal, a frequency-domain feature of the acceleration signal, a time-domain feature of the angular velocity signal, and/or a frequency-domain feature of the angular velocity signal; filtering the acceleration signal and the angular velocity signal to obtain a target acceleration signal and a target angular velocity signal of the user's cycling if it is determined, based on an analysis result, that a current status of the user is a cycling state; and determining a behavior feature of the user's cycling based on the target acceleration signal and the target angular velocity signal, where the behavior feature includes a cadence feature and a foot posture feature.

Abstract: This application relates to the field of electronic technologies, and provides an action recognition method and apparatus, a terminal device, and a motion monitoring system. Characteristic extraction and action recognition are performed based on motion data collected by data collection apparatuses; a gait characteristic, a swing gesture characteristic, and an image action characteristic of a user are recognized by using a plurality of pieces of motion data; and a type of a hitting action of a player is determined based on the gait characteristic, the swing gesture characteristic, and the image action characteristic. In this way, the hitting action of the player in a motion process can be accurately recognized. This is conducive to performing comprehensive analysis on a comprehensive sports capability of the player, and is more helpful to formulating a personalized training plan for the player.

Abstract: A digital predistortion (DPD) system includes an input configured to receive an input signal. In some examples, a first signal path configured to generate a first signal based on the input signal. In some examples, an error model provider configured to generate an error model signal modeled after a gate bias error voltage associated with the DPD system. In some examples, a first combiner configured to combine the first signal and the error model signal to generate a first intermediate signal, and the DPD system generates an output signal based at least on the first intermediate signal.

Abstract: A transmitter for a communication system comprises a digital pre-distortion (DPD) circuit and adaptation circuitry. The DPD circuit is configured to generate a digital intermediate signal by compensating an input signal for distortions resulting from an amplifier. The amplifier is configured to output an output signal based on the digital intermediate signal. The DPD circuit includes one or more an infinite impulse response (IIR) filters configured to implement a first transfer function based on a first parameter, and a second transfer function based on the first parameter and a time constant. The DPD circuit is configured to generate an adjustment signal based on the first transfer function and the second transfer function. The adaptation circuitry is configured to update the first parameter based on the adjustment signal, the input signal, and the output signal.

Abstract: The motion data monitoring method includes: collecting, by an electronic device, an angular velocity signal and an acceleration signal of a user; obtaining, by the electronic device, a waveform feature of the angular velocity signal based on the angular velocity signal, and obtaining a waveform feature of the acceleration signal based on the acceleration signal; determining, by the electronic device, a gait feature of the user according to the waveform feature of the angular velocity signal and the waveform feature of the acceleration signal, where the gait feature includes a duration of flight from off-ground of a foot of the user to touching of the ground; and determining, by the electronic device, motion data according to the gait feature, where the motion data includes a jump height.