Nowadays, Dopple's wireless earphones have touch buttons as a user inter- face, however these take in quite some physical space. Since the earphones are becoming smaller over the years, there is a need for a new type of touch- less user interface, that is smaller. In order to create that new user interface, research is done on the topic of remote gesture sensing with sensors that can t onto wireless earphones. When gestures are recognized by the system, a corresponding action can be taken, like for example pausing the music. Small infra-red imaging sensors are chosen as a solution to the problem. Its images are analysed by a trained image recognition neural network created with Python and Keras. This network takes an image as input and outputs a gesture. Each gesture is supposed be linked to an action in the new user interface. This report focusses on the retrieval of low resolution infra-red images and neural network training/machine learning. The APDS-9500, an already existing moving gesture sensor, is used as a comparison with regard to the new neural network technique. It achieves an accuracy of 92.3% with 5 dierent gestures. The AMG Grid Eye is an 8 by 8 pixel infra-red camera for which 5 gestures are trained. For raw images, 5 gestures are recognized with an accuracy of 79.2%. With the help of pre-processing in the form of contrast increasing and linear extrapolation the accuracy is increased up to 92.4%. The FLUKE 279 FC is a high resolution camera mounted on a mul- timeter of which the images are downscaled to 30 by 30 pixels. It is found out that while the size of the model increases, accuracy also increases up to 97.2% for 5 gestures. When the FLUKE is tested with 9 dierent gestures, while also optimizing for size, an accuracy is achieved of 98.2% with a model of 203 kB. The study proves that 30 by 30 infra-red images contain enough information to use gesture recognition with a small neural network.