Apparatus comprising means (21, 22, P) configured for: - obtaining (T2) a plurality of sensor data items, wherein a sensor data item specifies a set of values determined from at least one sensor, wherein the sensor data item specifies a time sequence from electromyography electrodes - training (T3), based on the plurality of sensor data items, a machine learning model configured for classifying sensor data items, wherein the machine learning model comprises: - a data adaptation part configured for determining a modified sensor data item (X') based on an input sensor data item (X), - an encoder configured for determining encoded features (F) based on the modified sensor data item (X'), - a decoder configured for determining a decoded sensor data item (Y) based on the encoded features (F), representing an estimation of the input sensor data item (X), and characterized in that the machine learning model further comprises: - a classifier configured for determining a hand gesture and a class (C) associated with the input sensor data item (X) based on the encoded features, - inferring (T4) a class associated with a sensor data item, based on the trained machine learning model , and - controlling (T5) the execution of an action based on the inferred class, thereby providing a hand gesture-based user interface, wherein training (T3) the machine learning model comprises updating parameters of the data adaptation part based on a comparison between an input sensor data item (X) from the plurality of sensor data items and the corresponding decoded sensor data item (Y), while maintaining the encoder, the decoder and the classifier frozen. - obtaining (T2) a plurality of sensor data items, wherein a sensor data item specifies a set of values determined from at least one sensor, wherein the sensor data item specifies a time sequence from electromyography electrodes - training (T3), based on the plurality of sensor data items, a machine learning model configured for classifying sensor data items, wherein the machine learning model comprises: - a data adaptation part configured for determining a modified sensor data item (X') based on an input sensor data item (X), - an encoder configured for determining encoded features (F) based on the modified sensor data item (X'), - a decoder configured for determining a decoded sensor data item (Y) based on the encoded features (F), representing an estimation of the input sensor data item (X), and characterized in that the machine learning model further comprises: - a classifier configured for determining a hand gesture and a class (C) associated with the input sensor data item (X) based on the encoded features, - inferring (T4) a class associated with a sensor data item, based on the trained machine learning model , and - controlling (T5) the execution of an action based on the inferred class, thereby providing a hand gesture-based user interface, - a classifier configured for determining a hand gesture and a class (C) associated with the input sensor data item (X) based on the encoded features, - inferring (T4) a class associated with a sensor data item, based on the trained machine learning model , and - controlling (T5) the execution of an action based on the inferred class, thereby providing a hand gesture-based user interface, wherein training (T3) the machine learning model comprises updating parameters of the data adaptation part based on a comparison between an input sensor data item (X) from the plurality of sensor data items and the corresponding decoded sensor data item (Y), while maintaining the encoder, the decoder and the classifier frozen. <EOS>