Can Gaze Beat Touch? A Fitts' Law Evaluation of Gaze, Touch, and Mouse Inputs

Gaze input has been a promising substitute for mouse input for point and select interactions. Individuals with severe motor and speech disabilities primarily rely on gaze input for communication. Gaze input also serves as a hands-free input modality in the scenarios of situationally-induced impairments and disabilities (SIIDs). Hence, the performance of gaze input has often been compared to mouse input through standardized performance evaluation procedure like the Fitts' Law. With the proliferation of touch-enabled devices such as smartphones, tablet PCs, or any computing device with a touch surface, it is also important to compare the performance of gaze input to touch input. In this study, we conducted ISO 9241-9 Fitts' Law evaluation to compare the performance of multimodal gaze and foot-based input to touch input in a standard desktop environment, while using mouse input as the baseline. From a study involving 12 participants, we found that the gaze input has the lowest throughput (2.55 bits/s), and the highest movement time (1.04 s) of the three inputs. In addition, though touch input involves maximum physical movements, it achieved the highest throughput (6.67 bits/s), the least movement time (0.5 s), and was the most preferred input. While there are similarities in how quickly pointing can be moved from source to target location when using both gaze and touch inputs, target selection consumes maximum time with gaze input. Hence, with a throughput that is over 160% higher than gaze, touch proves to be a superior input modality

Recognizing Interspersed sketches quickly

Sketch recognition is the automated recognition of hand-drawn diagrams. When allowing users to sketch as they would naturally, users may draw shapes in an interspersed manner, starting a second shape before finishing the first. In order to provide freedom to draw interspersed shapes, an exponential combination of subshapes must be considered. Because of this, most sketch recognition systems either choose not to handle interspersing, or handle only a limited pre-defined amount of interspersing. Our goal is to eliminate such interspersing drawing constraints from the sketcher. This paper presents a high-level recognition algorithm that, while still exponential, allows for complete interspersing freedom, running in near real-time through early effective sub-tree pruning. At the core of the algorithm is an indexing technique that takes advantage of geometric sketch recognition techniques to index each shape for efficient access and fast pruning during recognition. We have stresstested our algorithm to show that the system recognizes shapes in less than a second even with over a hundred candidate subshapes on screen.National Science Foundation (U.S.) (IIS Creative IT Grant #0757557

Creating the Perception-based LADDER sketch recognition language

Sketch recognition is automated understanding of hand-drawn diagrams. Current sketch recognition systems exist for only a handful of domains, which contain on the order of 10--20 shapes. Our goal was to create a generalized method for recognition that could work for many domains, increasing the number of shapes that could be recognized in real-time, while maintaining a high accuracy. In an effort to effectively recognize shapes while allowing drawing freedom (both drawing-style freedom and perceptually-valid variations), we created the shape description language modeled after the way people naturally describe shapes to 1) create an intuitive and easy to understand description, providing transparency to the underlying recognition process, and 2) to improve recognition by providing recognition flexibility (drawing freedom) that is aligned with how humans perceive shapes. This paper describes the results of a study performed to see how users naturally describe shapes. A sample of 35 subjects described or drew approximately 16 shapes each. Results show a common vocabulary related to Gestalt grouping and singularities. Results also show that perception, similarity, and context play an important role in how people describe shapes. This study resulted in a language (LADDER) that allows shape recognizers for any domain to be automatically generated from a single hand-drawn example of each shape. Sketch systems for over 30 different domains have been automatically generated based on this language. The largest domain contained 923 distinct shapes, and achieved a recognition accuracy of 83% (and a top-3 accuracy of 87%) on a corpus of over 11,000 sketches, which recognizes almost two orders of magnitude more shapes than any other existing system.National Science Foundation (U.S.) (grant 0757557)National Science Foundation (U.S.) (grant 0943499

Cardiac-directed expression of a catalytically inactive adenylyl cyclase 6 protects the heart from sustained β-adrenergic stimulation.

ObjectivesIncreased expression of adenylyl cyclase type 6 (AC6) has beneficial effects on the heart through cyclic adenosine monophosphate (cAMP)-dependent and cAMP-independent pathways. We previously generated a catalytically inactive mutant of AC6 (AC6mut) that has an attenuated response to β-adrenergic receptor stimulation, and, consequently, exhibits reduced myocardial cAMP generation. In the current study we test the hypothesis that cardiac-directed expression of AC6mut would protect the heart from sustained β-adrenergic receptor stimulation, a condition frequently encountered in patients with heart failure.Methods and resultsAC6mut mice and transgene negative siblings received osmotic mini-pumps to provide continuous isoproterenol infusion for seven days. Isoproterenol infusion caused deleterious effects that were attenuated by cardiac-directed AC6mut expression. Both groups showed reduced left ventricular (LV) ejection fraction, but the reduction was less in AC6mut mice (p = 0.047). In addition, AC6mut mice showed superior left ventricular function, manifested by higher values for LV peak +dP/dt (p = 0.03), LV peak -dP/dt (p = 0.008), end-systolic pressure-volume relationship (p = 0.003) and cardiac output (p<0.03). LV samples of AC6mut mice had more sarco/endoplasmic reticulum Ca2+-ATPase (SERCA2a) protein (p<0.01), which likely contributed to better LV function. AC6mut mice had lower rates of cardiac myocyte apoptosis (p = 0.016), reduced caspase 3/7 activity (p = 0.012) and increased B-cell lymphoma 2 (Bcl2) expression (p = 0.0001).ConclusionMice with cardiac-directed AC6mut expression weathered the deleterious effects of continuous isoproterenol infusion better than control mice, indicating cardiac protection

Creating and Implementing an Online Course Etiquette Appreciative Agreement: Recommendations and Insights for Updating Course Material and Social Expectations to Aid in the Transition to Online Learning During the COVID-19 Pandemic

A tech report created by the Texas A&M University Engineering Education Faculty and FriendsWith the threat of COVID-19 risking the health and safety of the 19.9 million students and 1.5 million faculty studying and working at colleges and universities across the U.S., these institutions have had little choice but to replace traditional, in-person classes with online, virtual alternatives. This unprecedented rapid adjustment has come with many unexpected difficulties as neither faculty nor students were prepared to teach or learn virtually, respectively. Beyond the obvious challenges of converting course material and accessing resources, an invisible difficulty lies in the lack of social expectations for this unfamiliar environment. A majority of the students and faculty do not know how to properly interact in an online setting. The home environment creates a completely different set of norms and expectations, many of which can be distracting and deleterious to the classroom environment. Thus, there is a critical need to provide students (and faculty) with a set of expectations to help set the tone of the virtual classroom. In the absence of such knowledge, the virtual classroom will be at a disadvantage for providing an effective learning environment, disenfranchising, and causing irreparable damage to the education of millions of students across the nation. As such, this document provides a recommended etiquette template for faculty to use in their classroom. This document has been implemented, tested, and improved across several courses at Texas A&M University. We are sharing this etiquette template so that other faculty at TAMU and other universities can use it to test the tone in their virtual classroom

Perceptually-based language to simplify sketch recognition user interface development

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 473-495).Diagrammatic sketching is a natural modality of human-computer interaction that can be used for a variety of tasks, for example, conceptual design. Sketch recognition systems are currently being developed for many domains. However, they require signal-processing expertise if they are to handle the intricacies of each domain, and they are time-consuming to build. Our goal is to enable user interface designers and domain experts who may not have expertise in sketch recognition to be able to build these sketch systems. We created and implemented a new framework (FLUID - f acilitating user interface development) in which developers can specify a domain description indicating how domain shapes are to be recognized, displayed, and edited. This description is then automatically transformed into a sketch recognition user interface for that domain. LADDER, a language using a perceptual vocabulary based on Gestalt principles, was developed to describe how to recognize, display, and edit domain shapes. A translator and a customizable recognition system (GUILD - a generator of user interfaces using ladder descriptions) are combined with a domain description to automatically create a domain specific recognition system.(cont.) With this new technology, by writing a domain description, developers are able to create a new sketch interface for a domain, greatly reducing the time and expertise for the task Continuing in pursuit of our goal to facilitate UI development, we noted that 1) human generated descriptions contained syntactic and conceptual errors, and that 2) it is more natural for a user to specify a shape by drawing it than by editing text. However, computer generated descriptions from a single drawn example are also flawed, as one cannot express all allowable variations in a single example. In response, we created a modification of the traditional model of active learning in which the system selectively generates its own near-miss examples and uses the human teacher as a source of labels. System generated near-misses offer a number of advantages. Human generated examples are tedious to create and may not expose problems in the current concept. It seems most effective for the near-miss examples to be generated by whichever learning participant (teacher or student) knows better where the deficiencies lie; this will allow the concepts to be more quickly and effectively refined.(cont.) When working in a closed domain such as this one, the computer learner knows exactly which conceptual uncertainties remain, and which hypotheses need to be tested and confirmed. The system uses these labeled examples to automatically build a LADDER shape description, using a modification of the version spaces algorithm that handles interrelated constraints, and which also has the ability to learn negative and disjunctive constraints.by Tracy Anne Hammond.Ph.D

The Real-Time Classification of Competency Swimming Activity Through Machine Learning

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device