Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.Comment: 6 pages, 4 figures, IEEE CBMS 201

Dwell-free input methods for people with motor impairments

Millions of individuals affected by disorders or injuries that cause severe motor impairments have difficulty performing compound manipulations using traditional input devices. This thesis first explores how effective various assistive technologies are for people with motor impairments. The following questions are studied: (1) What activities are performed? (2) What tools are used to support these activities? (3) What are the advantages and limitations of these tools? (4) How do users learn about and choose assistive technologies? (5) Why do users adopt or abandon certain tools? A qualitative study of fifteen people with motor impairments indicates that users have strong needs for efficient text entry and communication tools that are not met by existing technologies. To address these needs, this thesis proposes three dwell-free input methods, designed to improve the efficacy of target selection and text entry based on eye-tracking and head-tracking systems. They yield: (1) the Target Reverse Crossing selection mechanism, (2) the EyeSwipe eye-typing interface, and (3) the HGaze Typing interface. With Target Reverse Crossing, a user moves the cursor into a target and reverses over a goal to select it. This mechanism is significantly more efficient than dwell-time selection. Target Reverse Crossing is then adapted in EyeSwipe to delineate the start and end of a word that is eye-typed with a gaze path connecting the intermediate characters (as with traditional gesture typing). When compared with a dwell-based virtual keyboard, EyeSwipe affords higher text entry rates and a more comfortable interaction. Finally, HGaze Typing adds head gestures to gaze-path-based text entry to enable simple and explicit command activations. Results from a user study demonstrate that HGaze Typing has better performance and user satisfaction than a dwell-time method

GaVe: A webcam-based gaze vending interface using one-point calibration

Gaze input, i.e., information input via eye of users, represents a promising method for contact-free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a short one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time, i.e., the time duration that users look at a given Cluster/item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to quickly understand and know how to interact with the interface, and showed good performance, selecting a target item within a group of 12 items in 6.76 seconds on average. We provide design guidelines for GaVe and discuss the potentials of the system

A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.Comment: Extension to our paper A differential approach for gaze estimation with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine Intelligenc