Your Observations of Multi-Touch Usability

Overview

This paper will provide an overview and comparison of MS Surface and 3M Capacitive System.

Ease of Use

Each system has a different user interface. The MS Surface appears to be easier to use and more intuitive than the 3M Capacitive system. It seemed like navigating in the MS Surface was much easier than with the 3M. This may be biased because I spent more time observing the MS Surface.

Interaction Method

The main difference between the two systems is that MS Surface uses a projector to test what & where a finger/hand/object is placed and the 3M Capacitive system uses an electrical conductor to measure the touch with the electricity of the user. This allows for different uses for the two. The 3M Capacitive system can’t detect non-human objects touching the screen; MS Surface can. The interaction method for the MS Surface seemed to be superior than that of the 3M system. The MS Surface appeared to be able to interact with static, non-human objects where the 3M system was limited to human objects. I noticed that some objects were not being detected on the MS Surface but that was obviously an application level bug.

System Feedback

The response from the system caused by a touch seemed to be application specific and varied from app to app rather than system to system. There does not appear to be a noticeable difference between which one is better.

Orientation

The orientation for the 3M was much more apparent & limited than that of the MS Surface. The top/bottom/left/right on the MS Surface could be ambiguous and depend on the user’s perspective. The 3M system has a very clear top/bottom/left/right and these would not change based on the user’s perspective.

System Implications

The technology behind 3M system could be much more powerful than that of the MS Surface. The 3M system could better detect signals from a human body, such as temperature, pressure and potentially generate stress levels that the user is experiencing. This could be used in games or other applications. The MS Surface only measures shadows and this could limit it in some ways.

Discussion

• Capabilities of Capacitive technology
• Future of 3M & Surface multi-touch systems

Your Observations of Multi-Touch Usability

Overview

This paper will provide an overview and comparison of MS Surface and 3M Capacitive System.

Ease of Use

Each system has a different user interface. The MS Surface appears to be easier to use and more intuitive than the 3M Capacitive system. It seemed like navigating in the MS Surface was much easier than with the 3M. This may be biased because I spent more time observing the MS Surface.

Interaction Method

The main difference between the two systems is that MS Surface uses a projector to test what & where a finger/hand/object is placed and the 3M Capacitive system uses an electrical conductor to measure the touch with the electricity of the user. This allows for different uses for the two. The 3M Capacitive system can’t detect non-human objects touching the screen; MS Surface can. The interaction method for the MS Surface seemed to be superior than that of the 3M system. The MS Surface appeared to be able to interact with static, non-human objects where the 3M system was limited to human objects. I noticed that some objects were not being detected on the MS Surface but that was obviously an application level bug.

System Feedback

The response from the system caused by a touch seemed to be application specific and varied from app to app rather than system to system. There does not appear to be a noticeable difference between which one is better.

Orientation

The orientation for the 3M was much more apparent & limited than that of the MS Surface. The top/bottom/left/right on the MS Surface could be ambiguous and depend on the user’s perspective. The 3M system has a very clear top/bottom/left/right and these would not change based on the user’s perspective.

System Implications

The technology behind 3M system could be much more powerful than that of the MS Surface. The 3M system could better detect signals from a human body, such as temperature, pressure and potentially generate stress levels that the user is experiencing. This could be used in games or other applications. The MS Surface only measures shadows and this could limit it in some ways.

Discussion

• Capabilities of Capacitive technology
• Future of 3M & Surface multi-touch systems

Low-Cost Multi-Touch Sensing through Frustrated Total Internal Reflection

Overview

This paper discusses frustrated total internal reflection as a simple, inexpensive, and scalable technique for enabling high-resolution multi-touch sensing on rear-projected interactive surfaces. In more detail, they go over previous applications, provide implementation details, discuss results from their initial prototype, and outline future directions.

Previous Applications

This technique was used in application such as finger prints since at least the 1960’s, a painting application in the 1970’s, robotics, and other applications. This technique is widely known and has been around for a long time. The principle is also used for fiber optics. This is a pro since the technology is familiar and has been studied and implemented already. This brings down the cost for using this technique.

Implementation Details

Simple imaging techniques such as rectification, background subtraction, noise removal, and connected component analysis are used for each frame of data. Since these algorithms are widely known and used, this is a pro when it comes to implementing this technology. Since this technique is camera-based, this implies that there will be a drawback with various backgrounds and background lighting. This is a con for this technique compared to a capacitor based system that is not affected by background noise.

Results from Initial Prototype

There was a disparity from using a ¼” waveguide for their prototype, but they said there was no reason, other than ease of implementation, to use a smaller one. Their surface became contaminated easily after usage which caused problems. However, with the adaptable background algorithm, it will learn the noise and not consider it as a touch. Also, the system depends on the optical qualities of the object being sensed and this will cause the system to not detect non-human touch objects such as a mug or hand with glove. Also, a user with dry skin will have to push harder on the screen to achieve the success of a user without dry skin. The surface will have to be cleaned periodically to maintain accuracy. Also noted, is that some of these issues could be remedied by engineering a compliant surface overlay.

Future Directions

They discuss upgrading the system to differentiate between different points of contact. Currently, there is no way to distinguish if two finger came from the same hand.

Discussion

• Low-level algorithms to detect touch, light, background, etc...
• How does this technique compare to 3M & MS Surface systems?
• Adaptive algorithms to target user attributes: dry skin vs. non-dry skin

User-Defined Gestures for Surface Computing

Overview

This paper was about a study involving user-defined gestures for surface computing and they discuss developing a vocabulary based on the common gestures that users perform without any outside influence. They performed a study where they would show an action on the screen and the user would try to guess the gesture that caused the action. Further, they created a taxonomy, analyzed the results in detail, made observations, and detailed discussions about the experiment & findings that they discovered.

Developing a User-Defined Gesture Set

One idea presented was that users are not designers. They emphasize that care must be taken when building a gesture set using user defined gestures. Testing on a large number of people, they will get the general consensus as to what is the most popular gesture to cause a specific action, but it may not be the best gesture to cause that specific action. Another interesting point is that the feedback from a computer might change the next gesture, or the next primitive step of a gesture. For example, when a user puts his or her hand down, a ripple appears. If the ripple did not appear, they may have taken a different course. This may impact the experiment, but they stated that they removed feedback.

Discussion

• It would be interesting to see a study where the subjects were unaware that the testers were looking for them to provide the most logical gesture to cause an action
• User’s could be giving a UI and told to take make some gesture to cause an action (such as delete, move, cut, etc...) in a “dummy” app, then collect that data-this way the user would naturally try to do some gesture to cause the action
• Integrating past knowledge - learned from single point device - will bias the most “natural” form of gestures: it would be interesting to see if results are the same when people that are not computer literate were tested.
• Mixed UIs:
• Standard computer input along with multi-touch screen: this would be the most practical application for typical computer use. An example would be a small box in the corner with common tasks like cut, copy, paste, etc... (another type of shortcut) or drawing a question mark on the screen for help, drag and drop an item with fingers if a user finds it more convenient than doing it with a mouse, etc...