Designer Led Computational Approach To Generate Mappings For Devices With Low Gestural Resolution

We present an approach for the semi-automatic generation of gesture mappings for devices with low gestural resolution such as the Myo Armband, an off-the-shelf EMG capture device. As an exemplar interactive task, we use text-entry: a pervasive and highly complex interaction. We quantify data related to interaction combining systematic studies (i.e., error, speed, accuracy) and semistructured workshops with experts (e.g., cognitive load, heuristics). We then formalize these factors in a mathematical model and use optimization algorithms (i.e. simulated annealing) to find an optimum gesture mapping. We demonstrated our method in a text-entry application (i.e., complex interactive dialogue) comparing our approach with other computationally determined mappings using naive cost functions. Our results showed that the designers mapping (with all factors weighted by designers) presented a good balance on performance in all factors involved (speed, accuracy, comfort, memorability, etc.), consistently performing better than purely computational mappings. The results indicate that our hybrid approach can yield better results than either pure user-driven methodologies or pure data-driven approaches, for our application context featuring a large solution space and complex high-level factors.