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Herein, the use of autoencoder\u2010based dimensionality reduction is explored to simplify the design process. For this purpose, a data glove equipped with accelerometers is used to record high\u2010dimensional hand movement data that are thereafter reduced to 2D embeddings using autoencoders. The exploration and evaluation of the resulting embeddings suggest that autoencoders can be used to create meaningful low\u2010dimensional representations of complex human movement. The characteristics <jats:italic>generality<\/jats:italic>, <jats:italic>variability<\/jats:italic>, <jats:italic>connectivity<\/jats:italic>, and <jats:italic>distinguishability<\/jats:italic> are established and a guideline is provided for assessing low\u2010dimensional embeddings. Referring to these characteristics, system engineers can evaluate different input modalities and gestures for their specific interaction task. 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