Arm gesture interaction method and system of wearable device and electronic device

Abstract

The invention relates to the field of instruction interaction of wearable devices, and particularly discloses an arm gesture interaction method and system of a wearable device and an electronic device. According to the method, a sequence of an arm image and a signal oscillogram of a bio-electricity signal are processed through a convolutional neural network model, so that motion information in the image sequence and local correlation features in the signal oscillogram are extracted; and the feature vector and the feature matrix are effectively fused based on a likelihood maximized Gaussian density map, so that the subsequent classification result is more accurate. Thus, the gesture arm type can be recognized more accurately, the willingness expressed by the behaviors of the human body can be understood more accurately, and then more satisfactory experience feeling is brought to people.

Description

technical field [0001] The present invention relates to the field of instruction interaction of wearable devices, and more particularly, to a method, system and electronic device for arm gesture interaction of wearable devices. Background technique [0002] With the advancement of science and technology and the continuous improvement of living standards, smart wearable devices have gradually entered people's eyes and gradually integrated into people's lives, becoming a series of well-known electronic products. However, due to the wearable and portable characteristics of smart wearable devices, their size design is usually small, and they can only be equipped with a small touch screen or even no touch screen. This makes it difficult to use tap controls that are otherwise suitable for mobile phones in wearable devices. [0003] Therefore, in order to improve the user's experience when controlling wearable devices, in recent years, researchers have devoted themselves to the st...

AI Technical Summary

Problems solved by technology

Some existing solutions only perform specific recognition on the fine-grained level of the fingers or the coarse-grained level of the arm, and the accuracy and precision of the recognized gestures or arm types are not satisfactory.

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