论文标题
在Arduino上实施微小的机器学习模型33 BLE的手势和语音识别
Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition
论文作者
论文摘要
在本文中，手势识别和语音识别应用程序将在具有微小的机器学习（Tinyml）的嵌入式系统上实施。它具有3轴加速度计，3轴陀螺仪和3轴磁力计。手势识别提供了一种创新的方法非语言交流。它在人类计算机互动和手语中具有广泛的应用。在实施手势识别的情况下，Tinyml模型是从Edgeimpulse框架中训练并部署的，以进行手势识别，并基于手动运动，Arduino Nano 33具有6轴IMU的BLE设备可以找出手势运动的方向。演讲是一种交流方式。语音识别是计算机对人类言语的陈述或命令的一种方式。语音识别的主要目的是实现人与机器之间的沟通。在实施语音识别的情况下，Tinyml模型是从Edgeimpulse框架中训练和部署的，以进行语音识别，并基于人类发音的关键字，Arduino Nano 33具有内置麦克风的BLE设备可以使RGB LED发光，例如红色，绿色或蓝色，基于按键字词。在结果部分中获得并列出了每个应用程序的结果，并对结果进行了分析。
In this article gesture recognition and speech recognition applications are implemented on embedded systems with Tiny Machine Learning (TinyML). It features 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. The gesture recognition,provides an innovative approach nonverbal communication. It has wide applications in human-computer interaction and sign language. Here in the implementation of hand gesture recognition, TinyML model is trained and deployed from EdgeImpulse framework for hand gesture recognition and based on the hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find out the direction of movement of hand. The Speech is a mode of communication. Speech recognition is a way by which the statements or commands of human speech is understood by the computer which reacts accordingly. The main aim of speech recognition is to achieve communication between man and machine. Here in the implementation of speech recognition, TinyML model is trained and deployed from EdgeImpulse framework for speech recognition and based on the keywords pronounced by human, Arduino Nano 33 BLE device having built-in microphone can make an RGB LED glow like red, green or blue based on keyword pronounced. The results of each application are obtained and listed in the results section and given the analysis upon the results.
