
Vijay RajannaTexas A&M University | TAMU · Department of Computer Science and Engineering
Vijay Rajanna
Texas A&M University
Senior Research Engineer at Sensel
| Ph.D. in Computer Science
About
25
Publications
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Introduction
I am a Human-Computer Interaction and Machine Learning researcher. I graduated with a Doctoral Degree in Computer Science from Texas A&M University and was advised by Dr. Tracy Hammond.
I currently work at Sensel where we are creating the world's most advanced touch technology to revolutionize the way humans interact with the digital world. At Sensel, I develop new algorithms and features to enhance touch and sensor performance across a variety of hardware and use-cases.
Publications
Publications (25)
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 performan...
Text entry is extremely difficult or sometimes impossible in the scenarios of situationally-induced impairments and disabilities, and for individuals with motor impairments (physical impairments and disabilities) by birth or due to an injury. As a remedy, many rely on gaze typing with dwell-based selection as it allows for hands-free text entry. Ho...
Despite the utility of gaze gestures as an input method, there is a lack of guidelines available regarding how to design gaze gestures, what algorithms to use for gaze gesture recognition, and how these algorithms compare in terms of performance. To facilitate the development of applications that leverage gaze gestures, we have evaluated the perfor...
This paper presents a Fitts' law experiment and a clinical case study performed with a head-mounted display (HMD). The experiment compared gaze, foot, and head pointing. With the equipment setup we used, gaze was slower than the other pointing methods, especially in the lower visual field. Throughputs for gaze and foot pointing were lower than mous...
We investigate new media to improve how teams of students create and organize artifacts as they perform design. Some design artifacts are readymade-e.g., prior work, reference images, code framework repositories-while others are self-made-e.g., storyboards, mock ups, prototypes, and user study reports. We studied how computer science students use t...
Every day we encounter a variety of scenarios that lead to situationally induced impairments and disabilities, i.e., our hands are assumed to be engaged in a task, and hence unavailable for interacting with a computing device. For example, a surgeon performing an operation, a worker in a factory with greasy hands or wearing thick gloves, a person d...
Gaze and head tracking, or pointing, in head-mounted displays enables new input modalities for point-select tasks. We conducted a Fitts' law experiment with 41 subjects comparing head pointing and gaze pointing using a 300 ms dwell (n = 22) or click (n = 19) activation, with mouse input providing a baseline for both conditions. Gaze and head pointi...
Gaze gesture-based interactions on a computer are promising, but the existing systems are limited by the number of supported gestures, recognition accuracy, need to remember the stroke order, lack of extensibility, and so on. We present a gaze gesture-based interaction framework where a user can design gestures and associate them to appropriate com...
A highly secure, foolproof, user authentication system is still a primary focus of research in the field of User Privacy and Security. Shoulder-surfing is an act of spying when an authorized user is logging into a system, and is promoted by a malicious intent of gaining unauthorized access. We present a gaze-assisted user authentication system as a...
Recent advancements in eye tracking technology are driving the adoption of gaze-assisted interaction as a rich and accessible human-computer interaction paradigm. Gaze-assisted interaction serves as a contextual, non-invasive, and explicit control method for users without disabilities; for users with motor or speech impairments, text entry by gaze...
Failing to brush one's teeth regularly can have surprisingly serious health consequences, from periodontal disease to coronary heart disease to pancreatic cancer. This problem is especially worrying when caring for the elderly and/or individuals with dementia, as they often forget or are unable to perform standard health activities such as brushing...
Shoulder-surfing is the act of spying on an authorized user of a computer system with the malicious intent of gaining unauthorized access. Current solutions to address shoulder-surfing such as graphical passwords, gaze input, tactile interfaces, and so on are limited by low accuracy, lack of precise gaze-input, and susceptibility to video analysis...
Gaze Typing, a gaze-assisted text entry method, allows individuals with motor (arm, spine) impairments to enter text on a computer using a virtual keyboard and their gaze. Though gaze typing is widely accepted, this method is limited by its lower typing speed, higher error rate, and the resulting visual fatigue, since dwell-based key selection is u...
Intelligent tutoring systems (ITS) empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students' progress. Despite the advantages, existing ITS do not automatically assess how students engage in problem solving? How do they perceive various activities? and How much time they...
Recent developments in eye tracking technology are paving the way for gaze-driven interaction as the primary interaction modality. Despite successful efforts, existing solutions to the "Midas Touch" problem have two inherent issues: 1) lower accuracy, and 2) visual fatigue that are yet to be addressed. In this work we present GAWSCHI: a Gaze-Augmen...
Transforming gaze input into a rich and assistive interaction modality is one of the primary interests in eye tracking research. Gaze input in conjunction with traditional solutions to the "Midas Touch" problem, dwell time or a blink, is not matured enough to be widely adopted. In this regard, we present our preliminary work, a framework that achie...
A carefully planned, structured, and supervised physiotherapy program, following a surgery, is crucial for the successful diagnosis of physical injuries. Nearly 50 % of the surgeries fail due to unsupervised, and erroneous physiotherapy. The demand for a physiotherapist for an extended period is expensive to afford, and sometimes inaccessible. Rese...
Advances in ubiquitous computing technology improve workplace productivity, reduce physical exertion, but ultimately result in a sedentary work style. Sedentary behavior is associated with an increased risk of stress, obesity, and other health complications. Let Me Relax is a fully automated sedentary-state recognition framework using a smartwatch...
A recent trend in the popular health news is, reporting the dangers of prolonged inactivity in one's daily routine. The claims are wide in variety and aggressive in nature, linking a sedentary lifestyle with obesity and shortened lifespans [25]. Rather than enforcing an individual to perform a physical exercise for a predefined interval of time, we...
Questions
Questions (2)
Hi,
I have an experiment where there is a between subjects factor and a within-subjects factor. Assume that the between subjects factor is "Gander" with two levels - male and female. The within-subjects factor is "ExerciseType" with three levels - push-ups, squats, and crunches.
The Dependent variable is "HeartRate".
Now assume that "N" male participants perform each exercise and their heart rates are recorded after each exercise, and similarly "M" female participants perform the same set of exercises and their heart rates are recorded.
After the completion of the experiment, I remove recorded heart rate values that are above and below some threshold, because I know that those are due to an instrument error.
Hence, in each cell, I end up with some 'x' number of values, and the value of x changes for each combination of (Gender, ExerciseType).
Please refer to the attached images.
At this point, if I conduct mixed model repeated measures ANOVA in SPSS ( Analyze -> General Linear Model -> Repeated Measures), I do not receive any error though I was expecting that it might complain about the missing values.
So, what is happening within SPSS? Also, is such an analysis valid (I am curious because there are missing values)? If not, what other tests I should do?
Thank you.
I would like to know if I am choosing the right test.
Goal: Does the typing speed (dependent variable) differ between two types of keyboards.
Dependent Variable: Typing Speed.
Independent variables: Keyboard: KB1, KB2 [Between factor variable] Keysize: Small, Large [within factor varabile] KeyShape: Square, Circle [within factor varabile]
Keyboard: KB1, KB2 [Between factor variable]
Keysize: Small, Large [within factor varabile] KeyShape: Square, Circle [within factor varabile]
KeyShape: Square, Circle [within factor varabile]
I have two group of users, Group1 (20 users) tests Keyboard_1 (KB1), Group2 (20 users) tests keyboard_2 (KB2).
So, from my perspective, this is a 3 factor mixed model ANOVA.
2(keyboard) x 2 (keysize) x 2(keyshape)
Between_factor x WithinFactor x WithinFactor
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Reasons:
Group 1 tests KB1 in four combinations of keysize and keyshape.
(small, square), (small, circle), (large, square), (large, circle)
Similarly, Group tasks KB2 in four combinations of keysize and keyshape.
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To perform this analysis in SPSS, I'm choosing Analyze -> General Linear Model -> Repeated Measures.
Q1) Is my understanding right?
Q2) How to interpret the significance of a factor. For example, if "keysize" factor has P < 0.05 (significant), does it mean that the typing speed (IV) differs based on the keysize alone (small vs large) and not considering the other factors.
Q3) How to interpret the interactions? If interaction Keysize * Keyboard is not significant. Does it mean that multiple levels of keysize (small or large) do not cross lines (typing speed) for the multiple levels of the keyboard ( x axis - KB1, KB2). Also, in an interaction like A * B * C, is the last factor (C) always on the X axis?
Thanks in advance.