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Method and system for CSI-based fine-grained gesture recognition

机译:基于CSI的细粒度手势识别方法和系统

摘要

The invention provides a method for CSI-based fine-grained gesture recognition, wherein the method comprises the following steps: determining a start point, an end point, a velocity, a direction and/or an inflection point of at least one stroke gesture in multiple dimensions according to an eigenvalue of channel state information; dividing the strokes according to the start point, the end point, the velocity, the direction and/or the inflection point of the stroke using a machine learning method and forming a stroke sequence; building a stroke decipherment model according to frequencies of the strokes appearing in natural language rules and/or scientific language rules and/or connection rules between the strokes; and dividing and recognizing the stroke sequence as a letter sequence, a radical sequence, a numeral sequence and/or a pattern sequence conforming to the natural language rules and/or the scientific language rules using the stroke decipherment model. The present invention involves recognizing strokes of characters from finger gesture, and then recovering characters from the strokes, so as to enrich types of languages that can be recognized from finger gesture and enhance recognition accuracy of gesture writing.
机译:本发明提供一种用于基于CSI的细粒度手势识别的方法,其中该方法包括以下步骤:确定至少一个笔划手势的起点,终点,速度,方向和/或拐点。根据信道状态信息的特征值的多个维度;使用机器学习方法根据笔划的起点,终点,速度,方向和/或拐点划分笔划并形成笔划序列;根据自然语言规则和/或科学语言规则和/或笔画之间的连接规则中出现的笔画频率,建立笔画解密模型;使用所述笔划解密模型将所述笔划序列划分并识别为符合自然语言规则和/或科学语言规则的字母序列,部首序列,数字序列和/或模式序列。本发明涉及从手指手势识别字符笔划,然后从笔划恢复字符,以丰富可以从手指手势识别的语言类型并提高手势书写的识别精度。

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