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

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 317)

This bibliography lists 182 reports, articles and other documents introduced into the NASA scientific and technical information system in November, 1988

Classification of EEG signals on standing, walking and running dataset using LSTM-RNN

Undoubtedly one of the most important strands of the brain-computer interface (BCI) method is an alternate communication method via brain signals. BCI converts electroencephalogram (EEG) signals from a perception of activity in the brain into user action utilising software and hardware. BCI has piqued the interest of researchers in a wide range of disciplines, such as cognitive science, deep learning, pattern matching, drug treatment medicine, etc. Patients suffering from neuro and cognitive disorders can be assisted through BCI, potentially enabling communication via gestures or just mental imagination. In this paper, a novel combination of Discrete Wavelet Transform (DWT) for extracting the best features and Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted for classifying the EEG signals acquired during standing, walking and running on a treadmill. The dataset used is freely downloaded from Open Science Framework repository. The proposed DWT-LSTMRNN method delivers 96.7% accuracy while classifying four different signals, and thus has the potential to be investigated further on BCI competition datasets that will pave way for a real-time application

Assessment of Mental Workload: a Comparison of Machine Learning Methods and Subjective Assessment Techniques

Mental workload (MWL) measurement is a complex multidisciplinary research field. In the last 50 years of research endeavour, MWL measurement has mainly produced theory-driven models. Some of the reasons for justifying this trend includes the omnipresent uncertainty about how to define the construct of MWL and the limited use of datadriven research methodologies. This work presents novel research focused on the investigation of the capability of a selection of supervised Machine Learning (ML) classification techniques to produce data-driven computational models of MWL for the prediction of objective performance. These are then compared to two state-of-the-art subjective techniques for the assessment of MWL, namely the NASA Task Load Index and the Workload Profile, through an analysis of their concurrent and convergent validity. Findings show that the data-driven models generally tend to outperform the two baseline selected techniques

a mixed reality digital set up to support design for serviceability

Abstract Design for serviceability begins with understanding the customer needs related to availability, reliability, accessibility and visibility, and aims at designing optimized systems where maintenance operations are easy and intuitive in order to reduce the time to repair and service costs. However, service actions are difficult to predict in front of a traditional CAD model. In this context, digital manufacturing tools and virtual simulation technologies can be validly used to create mixed digital environments where service tasks can be simulated in advance to support product design and improve maintenance actions. Furthermore, the use of human monitoring sensors can be used to detect the stressful conditions and to optimize the human tasks. The paper proposes a mixed reality (MR) set-up where operators are digitalized and monitored to analyse both physical and cognitive ergonomics. It is useful to predict design criticalities and improve the global system design. An industrial case study has been developed in collaboration with CNH Industrial to demonstrate how the proposed set-up is used for design for serviceability, on the basis of experimental evidence

virtual maintenance simulation for socially sustainable serviceability

Abstract In order to achieve more sustainable development processes, industries need not only to improve energy efficiency and reduce costs, but also to increase the operators' wellbeing to promote social sustainability. In this context, the present research focuses on the definition of a methodology based on human-centred virtual simulation to improve the social sustainability of maintenance tasks by enhancing system design and improving its serviceability. It is based on the operators' involvement and the analysis of their needs from the early design stages on virtual mock-ups. The methodology proposed merges a protocol analysis for human factors assessment and an immersive virtual simulation where immersive serviceability simulations can be used during design phases. To demonstrate the effectiveness of the proposed method, an industrial use case has been carried out in collaboration with CNH Industrial