3 results about "Fingertip detection" patented technology

This application relates to a health detection method and system based on fingertip touch, comprising the following steps: using a fingertip detection device installed on a massage chair to emit light sources of different wavelengths to irradiate the fingertip skin and collect data on the changes in reflected light intensity; preprocessing the collected light intensity change data and extracting feature parameters from the preprocessed data based on the relationship between light intensity change and blood volume; calculating heart rate and blood oxygen saturation based on the feature parameters and generating a health detection report; and transmitting the generated health detection report to a mini-program server via encryption, allowing users to view the health detection report through a mobile mini-program.

This invention relates to a miniature detection system based on magnetic particle imaging, belonging to the field of medical inflammatory imaging technology. This invention is a miniature and simple device for magnetic particle imaging for fingertip detection. It uses a microcontroller as the control terminal, a receiving coil that is closer to the imaging aperture, and low-diameter driving and receiving coils. By improving the signal-to-noise ratio and reducing background interference signals, it effectively improves experimental efficiency and the sensitivity of the detection system, and greatly enhances portability.

The application relates to the technical field of fingertip detection, and discloses a real-time fingertip detection method and equipment based on a lightweight model, which comprises the following steps: collecting hand image data and constructing a hand skeleton constraint relationship database; performing hierarchical feature extraction and feature hierarchical adaptive fusion through a mixed density encoder network to generate a comprehensive feature map; performing feature mapping fusion to output initial fingertip position coordinate data; adopting a model lightweight technology to optimize the network structure of the mixed density encoder network to generate a lightweight fingertip detection model; performing interframe difference detection and adaptive confidence threshold adjustment to output a real-time updated fingertip accurate position coordinate set, thereby effectively suppressing the jitter phenomenon of single-frame detection results, providing a smooth and continuous fingertip trajectory, and improving the naturalness and comfort of human-computer interaction.