Abstract: This application provides a deep learning-based heart rate detection method and a wearable device, and relates to the field of communications technologies. According to this solution, a deep Attention network is trained by using PPG and ACC sensor signals as inputs and using heart rate information and exercise scenario information as model outputs, and heart rate detection is performed by using a model obtained through training. Because motion artifact noise is distributed differently in different scenarios, a nonlinear relationship between a scenario, noise, and a heart rate may be fitted by using a deep Attention network learning mechanism, so that data denoising processing and feature extraction can be adaptively implemented in different scenarios, to achieve effects of signal denoising, signal fusion, and complex scenario identification in a plurality of complex scenarios, thereby canceling interference of motion artifact noise to a PPG signal, and improving precision of heart rate detection.

Abstract: Embodiments of this application provide a method for displaying a two-dimensional code and an electronic device. The method is performed by an electronic device, and includes: obtaining first sensor data, where the first sensor data is collected at a first moment; if it is determined, based on the first sensor data, that a first action of a user corresponding to the first moment is a preset action, determining whether the user performs a wrist turning action within a preset duration range starting from the first moment; and displaying a preset two-dimensional code page when the user performs a wrist turning action within the preset duration range. In the method, a process of presenting a two-dimensional code by the user can be simple and efficient.

Abstract: This application provides a method for determining a foldable screen included angle and associated devices of the method, and relates to the field of terminal technologies.

Abstract: A rope jumping status detection method is provided, including: collecting first target exercise data in a first target time period by using one or more motion sensors, where the first target time period includes a first end moment, and the first end moment is used to identify an end moment of the first target time period; determining a rope jumping status in the first target time period based on the first target exercise data by using an iterative window width; determining a first quantity of rope jumping interruptions based on the rope jumping status in the first target time period, where the first quantity of rope jumping interruptions is used to identify a quantity of rope jumping interruptions in the first target time period; and displaying the first quantity of rope jumping interruptions at the first end moment.

Abstract: A fingerprint recognition method and an electronic device. The electronic device includes a touch screen and a fingerprint sensor. The electronic device displays a first interface and captures first fingerprint information that is input by a user at the first interface. The electronic device determines whether the touch screen is in a first state, and the first state is used to indicate that the touch screen is in a screen protector state. If the touch screen is in the first state, the electronic device restores the first fingerprint information by using a first preset artificial intelligence (AI) restoration model to obtain second fingerprint information. The electronic device determines that the second fingerprint information matches preset fingerprint information and displays a second interface. Quality of a fingerprint image in the second fingerprint information is higher than quality of a fingerprint image in the first fingerprint information.