VARK

After taking the test it was no surprise that my score was quite representative of how I think I learn. However, I don’t think it is objective as the questions are rather transparent so it is quite easy to gravitate towards what one thinks one would like to do in a particular situation. No doubt to avoid this the battery of questions would have to be much larger.

At any given time the learning approach we use may depend on the particular problem which we are confronted with. Under different circumstances, such as familiarity or the lack there of, a totally different approach may be deployed.

Motion Editing with Data Glove

Wai-Chun Lam, Feng Zou, Taku Komura

City University of Hong Kong 83 Tat Chee Ave Kowloon, Hong Kong

Comments:

Drew’s Blog

Manoj’s Blog

Summary:

This paper describes a method to edit captured human motion data with a data glove.

Motion editing is frequently used to generate mew motion data from existing captured data. This procedure can be applied to characters with different body sizes using retargeting. Overlapping and blending motions is achieved with motion wrapping. These methods are traditionally based on keyframe postures, where the editor determines when the posture must be edited. The changes are then determined with inverse kinematics. This approach has the following drawbacks: In order to determine the effect of a change the motion has to be replayed. Drastic changes need the insertion of several keyframes. Keyframe editing is a static editing method whereas motion is obviously dynamic.

A new dynamic editing method using a data glove has been therefore developed. First the real human motion data that is to be used for the basic motion is prepared. The animator then mimics the motion that is viewed to create a mapping function between the hand gestures and the real human motion data. New motions not present in the original data can then be created from hand gestures alone. This method maps finger movements to the actions of the whole body and is also able to function in real time for virtual reality, gaming etc.

A P5 data glove is used where the index and middle fingers correspond to the legs and shoulders of the collected human movement data. Matching is then made by synchronizing the tips and pits of the motion curves. This stage can be repeated if required to improve the mapping function. As the degrees of movement of the human body are much greater than those of the fingers, individual finger movements are mapped to generalized coordinates for the body. The arm motions are conjugate with opposite leg actions. During the capture and mapping stages, Fourier series expansion is used to eliminate noise and high frequency data. Additionally, the domain of the mapping function can be scaled to cope with new hand gestures outside the original domain.

Evaluation & Results:

An ordinary walking motion was captured for the model data. The animator was asked to mimic this action in order for the mapping function to be generated. The animator was then asked to execute a hopping zigzag running stride. The most prominent features noted in the resulting animation were the absence of lateral body tilt and foot contact with the ground. This indicated the need to extract specific constraints from the original data and preserve them in the newly generated motion. In its current form, for the newly generated motion to be realistic it has to have a similar topographic structure to the original data. As a next step, a more detailed joint matching approach between hand and body will be explored.

Discussion:

The use of a dynamic control system in place of a static one when dealing with a dynamic system is logically very sound. However, a walking finger puppet will only be able to directly influence the legs of the human model. All other dynamics will have to be slaved to the legs or independently controlled.

In order to understand the results more thoroughly a more rigorous comparison needs to be presented. A comparison to a traditional static keyframe approach would be useful to identify advantages and shortcomings.

However, a very interesting approach. To be able to control and modify a complex motion with a simple and intuitive action surely has many applications.

EyePoint: Practical Pointing and Selection

Using Gaze and Keyboard

Manu Kumar, Andreas Paepcke, Terry Winograd

Stanford University, HCI Group 353 Serra Mall, Stanford, CA 94305-9035 {sneaker, paepcke, winograd}@cs.stanford.edu

Comments:

Drew’s Blog

Manoj’s Blog

Summary:

The article presents EyePoint which is a combination of gaze based pointing and keyboard triggered selection. The goal is to develop a simple system which is accurate and fast enough to be a viable alternative to traditional devices such as the mouse. The motivation for the project was to use gaze information to augment traditional input devices, and not serve as a substitute. The visual modality was seen as an input channel, where its use for motor control would be directly at odds with the users natural inclination. This is best illustrated by traditional dwell based systems, which by their very nature have lacked performance rendering them as non viable alternatives.

In the development of EyePoint, research began exploring how users use the mouse for pointing. It was universally seen that the mouse was the preferred method for all object manipulation tasks. Furthermore, for any gaze based system to be viable it would have to be able to execute all the clicking actions such as single, double, right, over, etc. In contrast to deliberate motor actions such as mouse and keyboard input which need no disambiguation. Eye movements need filtering to remove micro and involuntary scads to avoid the midas touch effect. Current gaze based systems suffer from a variety of intrusive functional byproducts undermining their viability.

EyePoint, tries to overcome this by employing a two step progressive refinement procedure in order to compensate for the inaccuracy of eye gaze trackers. The user looks at the desired target and holds down the appropriate hot key for the desired click action. The area being looked at is zoomed the user looks again and releases the hot key to execute the action. Drag is a two step process, where the destination is selected with another hot key. To abort an action the user simply looks outside the zoom area. An interesting refinement is the projection of a focussing grid on the zoomed window to stabilize gaze.

The system is based on four principles:

1. Not slaving any action directly to eye movements.

2. Using zooming to overcome accuracy problems.

3. Fixation and smoothing algorithm.

4. Efficient activation mechanism.

Refinements:

1. Ensuring that the zoom window is bound by the limits of the screen.

2. An animated zoom the reduce secondary saccades.

3. A focussing grid overlay to reduce jitter.

4. A current gaze feedback marker was tested and seen to be distracting.

Evaluation:

20 participants. Three variables: Focus points. Gaze marker. without focus points

1. 10 - 15 min training phase

2. Web Study navigate through 30 pages.

Subjects thought speeds were comparable.

3. Pointing only task to click on the red balloon.

Subjects felt the mouse was faster and more accurate.

4. Mixed typing and pointing with a mouse only measuring the time to point not type.

Subjects liked the reduction in hand movement, but preferred the accuracy of the mouse

Results:

Web Study:

EyePoint with focus marker was 20% slower than the mouse.

EyePint with focus markers was 10%, without was 13% greater error than the mouse.

Balloon Study:

Not a great deal of difference in speed here.

10x the error of the mouse.

Mixed Study:

Again not much difference in times.

20x the error of the mouse.

Discussion:

EyePoint is a well thought out use of gaze controlled pointing. An improvement on previous overloaded approaches. This system maximizes the modalities natural attributes without overburdening them.

However, the traditional mouse uses a single button for several actions single double, drag. EyePoint replaces this with multiple buttons which surely is less economic. It may have been better to use a single hot key similar in function to the traditional model. This brings up the question of ease of use for disabled users.

In the conclusion the data gathered for test three is reinterpreted more favorably by making assumptions. Surely it would be simpler to repeat test three with Eye point to get a direct comparison.

Monocular Eye Tracker

First Impressions:

Reasonable comfortable to wear could be used for an extended period of time.

Does not impede vision

easy to set up

consistent pupil lock

Ideas to exploit the device:

I have always been interested to see what is being looked at while a particular task is being completed.

A good measure of sensory attention

could be a interesting to set up an accuracy evaluation task for the eye tracker itself

Limitations of the device:

auto calibration is a little too fast paced, which may be why accuracy is questionable particularly in the vertical domain.

The CyberGlove

First impressions:

Comfortable to wear, does not impede movement, could be used for an extended period of time.

Setup, calibration, getting data from separate joints and setting vibrating transducers seemed straight forward. I need to familiarize myself with C# syntax, but completing the assignment was not hard, particularly with sample code to follow.

Ideas to exploit the device:

Exploring what tactile features are used in object identification tasks.

Vibro-tactile feedback.

Limitations of the device:

Quick calibration was not very accurate.

Would be curious to determine its consistency.

It seems very effective for its purpose.

The Monocular Augmented Reality Goggles

First impressions:

Much more user friendly than the binocular pair. Very little adjustment required to see clearly. In particular, none of the double vision problem of the first device. Images were evenly visible throughout the display field. In spite of being monocular the sense of depth was greater than with the previous binocular system.

Ideas to exploit the device:

As before all sorts of application can be thought of, with either additions to or subtractions from the environment.

Limitations of the device:

similar to the binocular pair however to a less extent. The field of view may be a little narrower. However, much more usable.

The Binocular Augmented Reality Goggles

First impressions:

Adjustments are very important to get any result. The vertical and proximal positioning of the unit on one’s head combined with correct interocular separation are critical. The head strap also has to be secure for any slight movements of the unit on one’s head and all bets are off.

There was an initial double vision when looking at objects at different depths of field. This is due to a fixed convergence on the cameras and a fixed focal plane with the eye piece displays. A short time is required to accommodate in order to maintain integration of left and right images.

Furthermore, one has to adjust to exploring the environment with the use of head movements as eye movements are not possible.

Ideas to exploit the device:

All sorts of application can be thought of, with either additions to or subtractions from the environment.

Limitations of the device:

The field of view is narrower and the focal length is longer than normal which gives the impression that objects are further away than they really are. It is a little like looking through a telescope.

A light shield around the eye pieces to block off light leaking in would improve clarity. This would also eliminate competition from one’s peripheral vision (seeing around the outside of the device).

Contrast and color representation need experimentation. It was hard to see the yellow book on the table etc.

In spite of these comparatively trivial points a most interesting experience.