基于FDC2214的手势识别系统
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摘要
基于手势的交互方式在人机交互中发挥着越来越重要的作用,手势识别是大多数手势交互系统的核心技术。提出的手势识别系统采用STM32F103单片机最小系统作为主控芯片,结合FDCC2214电容式传感器,通过I2C方式采集FDC2214收到的电容值,在STM32中进行加权平均算法实现手势信号的训练与识别,最终由OLED液晶显示具体模式测试结果。系统通过训练模式,可以对训练人的手势进行存储记忆,进而在判决模式下实现对训练人猜拳游戏(石头、剪刀、布)和划拳游戏(1,2,3,4,5)的识别。经过测试,手势识别系统运行良好,识别率高,且能达到实时性的要求。
Gesture recognition is the core technology of most gesture interaction systems. The proposed gesture recognition system adopts STM32 F103 single-chip microcomputer minimum system as the main control chip,combines with FDCC2214 capacitive sensor,collects the capacitance value received by FDC2214 through IIC method,implements weighted average algorithm in STM32 to realize training and recognition of gesture signal,and the specific model test results is displayed on OLED LCD. Through training mode,the system can store the memory of the hand gestures of the trainees,and then realize the recognition of the training people's guessing games( stone,scissors,cloth) and stroke games( 1,2,3,4,5) in the judgment mode. After testing,the gesture recognition system works well,it has high recognition rate,and can meet the real-time requirements.
Gesture recognition is the core technology of most gesture interaction systems. The proposed gesture recognition system adopts STM32 F103 single-chip microcomputer minimum system as the main control chip,combines with FDCC2214 capacitive sensor,collects the capacitance value received by FDC2214 through IIC method,implements weighted average algorithm in STM32 to realize training and recognition of gesture signal,and the specific model test results is displayed on OLED LCD. Through training mode,the system can store the memory of the hand gestures of the trainees,and then realize the recognition of the training people's guessing games( stone,scissors,cloth) and stroke games( 1,2,3,4,5) in the judgment mode. After testing,the gesture recognition system works well,it has high recognition rate,and can meet the real-time requirements.
引文
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[2]杜克峰.基于Zynq的手势识别系统的研究[D].大连:大连海事大学,2016.
[3]焦家祥.手势识别技术前沿概述[J].电子世界,2018(15):29-30.
[4] Hasan Haitham,Abdul-Kareem Sameem. Retraction note to:Humancomputer interaction using vision-based hand gesture recognition systems:A survey[J]. Neural Computing and Applications,2017,28(4):851-852.
[5] Xi Yulong,Cho Seoungjae. Gesture recognition method using sensing blocks[J]. Wireless Personal Communications,2016,91(4):1779-1797.
[6]曾亿山,刘睿.基于六轴传感器的无线动作识别系统设计[J].传感器与微系统,2018,37(10):98-101.
[7]韩韧,金永威,王强.基于STM32和超声波测距的倒车雷达预警系统设计[J].传感器与微系统,2016,35(4):63-66.
[8]谢檬,郭霞.智能小车控制系统设计[J].传感器与微系统,2016,35(12):110-112.
[9]陶睿杰,章征宇,陈蕾.基于XC7A35T与STM32F103RCT6的智能语音小车系统设计[J].电子设计工程,2018,26(3):170-174.
[10]董嘉棋,万卫兵,朱特浩,等.面向翻书机器人的手势控制指令的识别算法[J].东华大学学报:自然科学版,2017,43(6):869-877.
[11]陈春铁.基于虚拟现实的人机交互中的多特征手势识别追踪算法的研究[J].软件工程,2017,20(12):23-25.