  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.
