An Analytical Framework to Measure Effective Human Machine Interaction

Initially, human machine interface (HMI) was understood as the hardware and software through which human and machine could communicate. Gradually, is being recognized that many human factors such as usability, emotion, user’s physical and cognitive characteristics, domain knowledge, contribute as much to the effectiveness and efficiency of HMis as robust, reliable and sophisticated algorithms do. Clearly, both the human centered factors and the technical factors have direct or indirect relations with the effectiveness of the HMT. Nevertheless, the degree of influence of these factors on the effectiveness of human machine interaction is not well understood. Most of the work in the human machine interaction area is focused on creating and refining techniques and algorithms, application-driven efforts, or heuristic procedures, but there is a lack of basic or foundational work. In this work, we present a novel development of an accessible interface for the control of a robotic arm based on natural, effortless hand gestures designed for students with mobility impairments, and we provide a systematic framework to measure the effectiveness of this interface.