Method for performing accurate three-dimensional modeling based on data glove by using natural gestures

Abstract

The invention discloses a method for performing accurate three-dimensional modeling based on a data glove by using natural gestures. LilyPad Arduino is additionally arranged outside the glove as a processing chip, hand gestures are monitored through bending sensors, FSR402 type resistive thin film pressure sensors and a JY-901 module, and LilyPad XBee and an XBee 1mW Wire Antenna wireless module perform two-way communication with a computer. Hand coordinates obtained by somatosensory equipment are fused with motion data, so that a human body natural language can be understood by the computer, the measurement accuracy can reach that of a mouse, and the three-dimensional modeling of the user with the natural language can be implemented.

Description

technical field [0001] The invention relates to a method for accurate three-dimensional modeling based on data gloves using natural gestures. Background technique [0002] At present, wearable sensors such as bend sensors and accelerometers can already be used in combination to detect basic natural gestures such as bending and straightening of fingers. At the same time, combined with the capture of a large amount of natural gesture data and machine learning, it is now possible to perform gesture recognition with basic finger joint precision. However, the spatial movement of the hand will cause the cumulative error effect of the three-axis accelerometer, resulting in the inability to accurately restore the spatial trajectory of the hand, and directly affecting the judgment of the relative position relationship between the hand and the user's body. This makes the current data gloves can only be applied to gesture recognition of simple finger movements very limitedly, and cann...

AI Technical Summary

Problems solved by technology

[0004] Single-use somatosensory devices cannot recognize complex gesture languages, and the measurement accuracy of hands cannot meet the requirements of 3D modeling

However, a single data glove can only recognize gestures because it cannot restore the trajectory and positional relationship, and because it only uses hand posture instead of the analysis of the whole body skeleton, its interaction is very single, and the effect of applying it in 3D modeling is poor.

At the same time, neither somatosensory technology nor traditional gesture gloves can solve the problem of a large number of clicks and lack direct feedback to the human body.

Images