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In this article gesture recognition and speech recognition applications areimplemented on embedded systems with Tiny Machine Learning (TinyML). Itfeatures 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. Thegesture recognition,provides an innovative approach nonverbal communication. Ithas wide applications in human-computer interaction and sign language. Here inthe implementation of hand gesture recognition, TinyML model is trained anddeployed from EdgeImpulse framework for hand gesture recognition and based onthe hand movements, Arduino Nano 33 BLE device having 6-axis IMU can find outthe 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 speechis understood by the computer which reacts accordingly. The main aim of speechrecognition is to achieve communication between man and machine. Here in theimplementation of speech recognition, TinyML model is trained and deployed fromEdgeImpulse framework for speech recognition and based on the keywordspronounced by human, Arduino Nano 33 BLE device having built-in microphone canmake an RGB LED glow like red, green or blue based on keyword pronounced. Theresults of each application are obtained and listed in the results section andgiven the analysis upon the results.

Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition