Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People

We have applied interactive machine learning (IML) to the creation and customisation of gesturally controlled musical interfaces in six workshops with people with learning and physical disabilities. Our observations and discussions with participants demonstrate the utility of IML as a tool for participatory design of accessible interfaces. This work has also led to a better understanding of challenges in end-user training of learning models, of how people develop personalised interaction strategies with different types of pre-trained interfaces, and of how properties of control spaces and input devices influence people's customisation strategies and engagement with instruments. This work has also uncovered similarities between the musical goals and practices of disabled people and those of expert musicians.