A computer-implemented method assessment for identifying a user motor task for output via an output interface arranged to provide data to a computing device, the method comprising: a machine learning system (122, 508) that has been trained by: receiving labeled videos of motor tasks comprising location-dependent local motion features in the video associated with the video labels, the location-dependent local motion features comprising one of velocity or acceleration of an image element, wherein the image element is a contiguous sub-volume of the video, thus selecting a space-time window of the video depicting a scene element; and training the machine learning system using the labeled videos; receiving an unseen video depicting at least part of a user performing a motor task; inputting the unseen video to the trained machine learning system (122, 508) to identify the motor task being performed by the user; wherein the unseen video comprises multiple sequences of location-dependent local motion features of videos that are learnt; using the trained machine-learning system to predict, based on the location-dependent local motion features in the unseen video, the class label of the local motion feature selected from a plurality of possible motor task classes to be assigned to the unseen video based on a classification of the motor task using one or more class labels; wherein the machine-learning system comprises a random decision forest with one or more decision trees having a root node and a plurality of split nodes and a plurality of leaf nodes; wherein the class label identifies one or more possible motor task classes of the plurality of classes to which the motor task is predicted to belong based on the motor task classification data and at least one of the multiple sequences of local motion features in the unseen video detected by the trained machine learning system (122, 508), outputting the motor task classification data for the class of motor task that was performed by the user a machine learning system (122, 508) that has been trained by: receiving labeled videos of motor tasks comprising location-dependent local motion features in the video associated with the video labels, the location-dependent local motion features comprising one of velocity or acceleration of an image element, wherein the image element is a contiguous sub-volume of the video, thus selecting a space-time window of the video depicting a scene element; and training the machine learning system using the labeled videos; receiving an unseen video depicting at least part of a user performing a motor task; inputting the unseen video to the trained machine learning system (122, 508) to identify the motor task being performed by the user; wherein the unseen video comprises multiple sequences of location-dependent local motion features of videos that are learnt; using the trained machine-learning system to predict, based on the location-dependent local motion features in the unseen video, the class label of the local motion feature selected from a plurality of possible motor task classes to be assigned to the unseen video based on a classification of the motor task using one or more class labels; wherein the machine-learning system comprises a random decision forest with one or more decision trees having a root node and a plurality of split nodes and a plurality of leaf nodes; receiving labeled videos of motor tasks comprising location-dependent local motion features in the video associated with the video labels, the location-dependent local motion features comprising one of velocity or acceleration of an image element, wherein the image element is a contiguous sub-volume of the video, thus selecting a space-time window of the video depicting a scene element; and training the machine learning system using the labeled videos; receiving an unseen video depicting at least part of a user performing a motor task; inputting the unseen video to the trained machine learning system (122, 508) to identify the motor task being performed by the user; wherein the unseen video comprises multiple sequences of location-dependent local motion features of videos that are learnt; using the trained machine-learning system to predict, based on the location-dependent local motion features in the unseen video, the class label of the local motion feature selected from a plurality of possible motor task classes to be assigned to the unseen video based on a classification of the motor task using one or more class labels; wherein the machine-learning system comprises a random decision forest with one or more decision trees having a root node and a plurality of split nodes and a plurality of leaf nodes; wherein the class label identifies one or more possible motor task classes of the plurality of classes to which the motor task is predicted to belong based on the motor task classification data and at least one of the multiple sequences of local motion features in the unseen video detected by the trained machine learning system (122, 508), outputting the motor task classification data for the class of motor task that was performed by the user