10 results about "Fingertip detection" patented technology

The invention provides a palm center input method and device of intelligent wearable equipment, the intelligent wearable equipment and a computer readable storage medium. The palm center input method of the intelligent wearable device comprises the steps that a scene image is collected through an RGB camera of the intelligent wearable device for projection palm area recognition; after virtual input interface projection is carried out in the projection palm area, a first event image of a virtual input interface is acquired through an event camera of the intelligent wearable device; performing fingertip detection based on an event image set including the first event image through a trained fingertip detection model to obtain an operation fingertip position of the virtual input interface; and performing input processing based on the operation fingertip position and the virtual input interface. According to the method and the device, the problem of visual smear or ghosting caused by camera lag can be reduced, so that the operation fingertip position of a user in a high-dynamic scene can be accurately captured, and the interaction accuracy and fluency of the intelligent wearable equipment are improved.

The utility model discloses a finger tip passive rehabilitation device after a TIR (total infrared ray) operation, a rehabilitation training piece comprises a palm sleeve capable of being worn on the hand of a patient, the palm sleeve is provided with a finger training piece corresponding to fingers, the finger training piece comprises an annular piece and a finger sleeve which are sequentially arranged, the annular piece is connected with the palm sleeve through a finger connecting piece, and the finger sleeve is connected with the palm sleeve through a finger connecting piece. Finger tip detection pieces are arranged at the ends of the finger sleeves, a detection control piece is arranged on the back of the palm sleeve, the detection control piece is connected with the finger tip detection pieces, and a display area and a voice prompt structure are arranged on the detection control piece. According to the TIR post-operation finger tip passive rehabilitation device, in the massage rehabilitation operation process, a patient operates according to the prompt of the voice prompt structure on the detection control piece, the finger tip detection piece can sense the finger tip hyperemia and the finger tip blood sample saturation of the patient, and the finger tip detection piece can detect the finger tip hyperemia and the finger tip blood sample saturation. The display area can display the blood sample saturation and rehabilitation training conditions of the patient, disease rehabilitation is facilitated, and postoperative complications are reduced.

The present invention relates to the technical field of human-computer interaction, and in particular to a fingertip detection method based on visual features. Two sets of images are captured, one is a hand image with a background having a color difference from the hand color, and the other is a background image in a complex environment without a hand. The images are processed to obtain a hand edge image and a background edge image. The rotation-invariant HOG features of the edge images are used, and effective features are classified through AdaBoost ensemble learning training. A rotated offset vector is used to locate the fingertip, realizing the accurate detection of the fingertip point of a single finger in a complex environment.

The invention discloses a gesture recognition method and device and a gesture control method and device. The gesture recognition method comprises the following steps: acquiring object gesture image data; performing fingertip detection on the gesture image data to obtain a fingertip recognition feature vector; performing feature extraction on the gesture image data based on a gesture classification model to obtain a gesture recognition feature vector; and fusing the fingertip recognition feature vector and the gesture recognition feature vector in a decision-making layer, and performing analysis processing to obtain a gesture recognition result. The gesture recognition accuracy can be improved.

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.

The invention relates to the technical field of fingertip detection, and discloses a real-time fingertip detection method and device based on a lightweight model, and the method comprises the steps: collecting hand image data, and constructing a hand skeleton constraint relation database; performing hierarchical feature extraction and feature hierarchical adaptive fusion through a mixed density encoder network to generate a comprehensive feature map; carrying out feature mapping fusion, and outputting initial fingertip position coordinate data; performing network structure optimization on the mixed density encoder network by adopting a model lightweight technology to generate a lightweight fingertip detection model; according to the method, inter-frame differential detection and self-adaptive confidence threshold adjustment are executed, and a fingertip accurate position coordinate set updated in real time is output, so that the jitter phenomenon of a single-frame detection result is effectively inhibited, a smooth and continuous fingertip track is provided, and the naturalness and comfort of man-machine interaction are improved.

The invention relates to a health detection method and system based on fingertip touch, and the method comprises the following steps: using a fingertip detection device installed on a massage chair to emit light sources with different wavelengths to irradiate fingertip skin, and collecting reflected light intensity change data; preprocessing the collected light intensity change data, and extracting characteristic parameters from the preprocessed data according to the relationship between the light intensity change and the blood volume; calculating heart rate and oxyhemoglobin saturation according to the characteristic parameters, and generating a health detection report; and the generated health detection report is transmitted to an applet server side through encryption, and a user checks the health detection report through a mobile phone applet.

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.

The application relates to the technical field of intelligent human-computer interaction, in particular to an aerial handwritten text collection method based on an improved yolov5 network and monocular vision, which comprises the following steps: constructing an initial fingertip dataset and performing pretreatment to obtain a target fingertip dataset, improving the yolov5 network based on an efficient pyramid segmentation attention module EPSA and a weighted bidirectional feature pyramid network BiFPN, obtaining an improved yolov5 network, inputting a training set of the target fingertip dataset into the improved yolov5 network to obtain a fingertip detection model, obtaining real-time two-dimensional video images of aerial handwritten text based on a monocular camera and inputting the fingertip detection model, and forming the aerial handwritten text based on a coordinate system virtual sliding technology, so that the technical problems that, in the prior art, a 3D sensor is large and expensive, resulting in insufficient universality, the requirement for a WIFI environment is relatively strict, resulting in relatively large limitation, and a fingertip is a small target, resulting in low detection accuracy and high omission rate when the yolov5 network detects the fingertip are solved.