Exploring Human Computer Interaction and its Implications on Modeling for Individuals with Disabilities

Computers provide an interface to the world for many individuals with disabilities and without effective computer access, quality of life may be severely diminished. As a result of this dependence, optimal human computer interaction (HCI) between a user and their computer is of paramount importance. Optimal HCI for individuals with disabilities relies on both the existence of products which provide the desired functionality and the selection of appropriate products and training methods for a given individual. From a product availability standpoint, optimal HCI often depends on modeling techniques used during the development process to evaluate a design, assess usability and predict performance. Computer access evaluations are often too brief in duration and depend on the products present at the site of the evaluation. Models could assist clinicians in dealing with the problems of limited time with clients, limited products for the client to trial, and the seemingly unlimited system configurations available with many potential solutions. Current HCI modeling techniques have been developed and applied to the performance of able-bodied individuals. Research concerning modeling performance for individuals with disabilities has been limited. This study explores HCI as it applies to both able-bodied and individuals with disabilities. Eleven participants (5 able-bodied / 6 with disabilities) were recruited and asked to transcribe sentences presented by a text entry interface supporting word prediction with the use of an on-screen keyboard while time stamped keystroke and eye fixation data was collected. Data was examined to identify sequences of behavior, performance changes based on experience, and performance differences between able-bodied and participants with disabilities. The feasibility of creating models based on the collected data was explored. A modeling technique must support selection from multiple sequences of behavior to perform a particular type of action and variation in execution time for primitive operations in addition to handling errors. The primary contributions made by this study were knowledge gained relative to the design of the test bench and experimental protocol

Identification of typing behaviors from large keystroke dataset

In this thesis work, keystroke-level typing data of over 168000 participants are analyzed to understand determinants of transcription typing behaviors. Keystroke patterns are analyzed in detail and linked to typing performance. Inter-Key Intervals of letter pairs and other statistical indicators of typing performance are calculated and their distributions and statistical relations are studied. These analyses show, among other findings, that Inter-Key Intervals in typing distant letter pairs in the keyboard are more predictive than other letter pairs, e.g. letter repetitions. Rollover typing, where the next key is pressed before the previous key is released, is prevalent widely, linked to faster typing with high correlation. Finally, medoids-based (PAM) unsupervised clustering of participants is performed to identify groups of typists with similar typing characteristics, and the findings from the clusters are interpreted in terms of performance, accuracy, hand movements and rollover behaviors

The Effect of Tactile and Audio Feedback in Handheld Mobile Text Entry

Effects of tactile and audio feedback are examined in the context of touchscreen and mobile use. Prior experimental research is graphically summarized by task type (handheld text entry, tabletop text entry, non-text input), tactile feedback type (active, passive), and significant findings, revealing a research gap evaluating passive tactile feedback in handheld text entry (a.k.a. texting ). A passive custom tactile overlay is evaluated in a new experiment wherein 24 participants perform a handheld text entry task on an iPhone under four tactile and audio feedback conditions with measures of text entry speed and accuracy. Results indicate audio feedback produces better performance, while the tactile overlay degrades performance, consistent with reviewed literature. Contrary to previous findings, the combined feedback condition did not produce improved performance. Findings are discussed in light of skill-based behavior and feed-forward control principles described by Gibson (1966) and Rasmussen (1983)

Modelling the dynamics of the piano action: is apparent success real?

International audienceThe kinematics and the dynamics of the piano action mechanism have been much studied in the last 50 years and fairly sophisticated models have been proposed in the last decade. Surprisingly, simple as well as sophisticated models seem to yield very valuable simulations when compared to measurements. We propose here a very simple model, with only 1-degree of freedom, and compare its outcome with force and motion measurements obtained by playing a real piano mechanism. The model, purposely chosen as obviously too simple to be predictive of the dynamics of the grand piano action, appears either as very good or as very bad, depending on which physical quantities are used as the input and output. We discuss the sensitivity of the simulation results to the initial conditions and to noise and the sensitivity of the experimental/simulation comparisons to the chosen dynamical model. It is shown that force-driven simulations with position comparisons, as they are proposed in the literature, do not validate the dynamical models of the piano action. It is suggested that these models be validated with position-driven simulations and force comparisons

Assessing the Accuracy of Task Time Prediction of an Emerging Human Performance Modeling Software - CogTool

There is a need for a human performance modeling tool which not only has the ability to accurately estimate skilled user task time for any interface design, but can be used by modelers with little or no programming knowledge and at a minimal cost. To fulfill this need, this research investigated the accuracy of task time prediction of a modeling tool – CogTool - on two versions of an interface design used extensively in the petrochemical industry – DeltaV. CogTool uses the KeyStroke Level Model (KLM) to calculate and generate time predictions based on specified operators. The data collected from a previous study (Koffskey, Ikuma, & Harvey, 2013) that investigated how human participants (24 students and 4 operators) performed on these interfaces (in terms of mean speed in seconds) were compared to CogTool’s numeric time estimate. Three tasks (pump I, pump II and cascade system failures) on each interface for both participant groups were tested on both interfaces (improved and poor), on the general hypothesis that CogTool will make task time predictions for each of the modeled tasks, within a certain range of what actual human participants had demonstrated. The 95% confidence interval (CI) tests of the means were used to determine if the predictions fall within the intervals. The estimated task time from CogTool did not fall within the 95% CI in 9 of 12 cases. Of the 3 that were contained in the acceptable interval, two belonged to the experienced operator group for tasks performed on the improved interface, implying that CogTool was better in predicting the operators’ performance than the students’. A control room monitoring task, by its nature, places great demand on an operator’s mental capacity. This also includes the fact that operators work on multiple screens and/or consoles, sometimes requiring them to commit information to memory that they have to revisit a screen to check on some vital information. In this regard, it is suggested that the one user mental operator for “think time” (estimated as 1.2sec), should be revised in CogTool to accommodate the demand on the operator. For this reason, the present CogTool prediction did not meet expectations in estimating control room operator task time, but it however succeeded in showing where the poor interface could be improved by comparing the detailed steps to the improved interface

Identification of User Behavioural Biometrics for Authentication using Keystroke Dynamics and Machine Learning

This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively. In addition, mRMR feature selection has been applied to increase the classification performance metrics and to confirm the identity of the users based on the way they access computing devices. From the results, we conclude that device information and touch pressure effectively contribute to identifying each user. Out of them, features that contain device information are responsible for increasing the performance metrics of the system by adding a token-based authentication layer. Based upon the results, random forest yields better classification results for this dataset. The research will contribute significantly to the field of cyber-security by forming a robust authentication system using machine learning algorithms

Endogenous sources of interbrain synchrony in duetting pianists

When people interact with each other, their brains synchronise. However, it remains unclear whether interbrain synchrony (IBS) is functionally relevant for social interaction or stems from exposure of individual brains to identical sensorimotor information. To disentangle these views, the current dual-EEG study investigated amplitude-based IBS in pianists jointly performing duets containing a silent pause followed by a tempo change. First, we manipulated the similarity of the anticipated tempo change and measured IBS during the pause, hence, capturing the alignment of purely endogenous, temporal plans without sound or movement. Notably, right posterior gamma IBS was higher when partners planned similar tempi, it predicted whether partners’ tempi matched after the pause, and was modulated only in real, not in surrogate pairs. Second, we manipulated the familiarity with the partner’s actions and measured IBS during joint performance with sound. Although sensorimotor information was similar across conditions, gamma IBS was higher when partners were unfamiliar with each other’s part and had to attend more closely to the sound of the performance. These combined findings demonstrate that IBS is not merely an epiphenomenon of shared sensorimotor information, but can also hinge on endogenous, cognitive processes crucial for behavioural synchrony and successful social interaction