DEL10.10.02.D93 Innovation Analysis Report 2013

Abstract

ATCOs spend a significant amount of their time interacting or inputting data into the system. With the increase in the level of traffic, the time spent inputting data may reach a point where it is no more compatible with the time pressure of the job. Looking for innovative interaction means for easing the interactions and reducing the duration of the inputs is thus a challenge that should be taken over for preparing the future of the ATM. This document addresses this challenge by looking at three technologies: multi-touch screens, eye-tracking and handwriting recognition. The approach taken for analysing these technologies is a combination of literature reviews and experiments. Experiment results are used for underlining the benefits of the technologies, and the guidelines for deploying them. The overall assessment of the investigation comparing multi-touch vs. mouse revealed a trend that further developing of controller working positions with multi-touch interaction philosophy is worth to follow up. 2 proposals were evaluated. The technology was found to be quick and efficient. The results offer a valuable baseline for further developments. Other evaluations are required to compare the solutions and assess the impacts on the KPI. The accuracy and efficiency of the eye-tracking was assessed. Eye-tracking offers a promising interaction mean for speeding up the selection of aircraft and some actions such as assuming an aircraft. The accuracy is compatible with the size of track labels. Pointing smaller graphical objects should not be considered. Experiments on the benefits related to the decrease in the duration of the inputs underline a tendency to reduce by an average of 30 % the duration of the assume action. This experiment should be replicated to ensure the validity of the results. Last, hand writing recognition, the ability to read characters and translate them into some form of code, provides a natural way of entering information into the system and could increase the human machine symbiosis. This report proposes criteria for selecting the appropriate hand-writing recognition engine. An experiment was performed with four hand writing products to assess their accuracy. Results highlight that one product is particularly above the others with a mean recognition rate of 93%. Some improvements are still required on the recognition rate before using it on a controller working position. All these technologies have interesting benefits. The fact of integrating several of these technologies on the same position could be the solution for building a new HMI concept to face the future challenges of ATM.