Many interactive systems are susceptible to misinterpreting the user's input actions or gestures. Interpretation errors are common when systems gather a series of signals from the user and then attempt to interpret the user's intention based on those signals -- e.g., gesture identification from a touchscreen, camera, or body-worn electrodes -- and previous work has shown that interpretation error can cause significant problems for learning new input commands. Error-reduction strategies from telecommunications, such as repeating a command or increasing the length of the input while reducing its expressiveness, could improve these input mechanisms -- but little is known about whether longer command sequences will cause problems for users (e.g., increased effort or reduced learning). We tested performance, learning, and perceived effort in a crowd-sourced study where participants learned and used input mechanisms with different error-reduction techniques. We found that error reduction techniques are feasible, can outperform error-prone ordinary input, and do not negatively affect learning or perceived effort.
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)