Blog #23: User-Defined Motion Gestures for Mobile Interaction

User-Defined Motion Gestures for Mobile Interaction

Authors : Jaime Ruiz, Yang Li, and Edward Lank

Authors Bios:

• Jaime Ruiz is a PhD student at the University of Waterloo and has been a visiting researcher at the Palo Alto Research Center.
• Yang Li is a researcher at Google and earned his PhD from the Chinese Academy of Sciences.
• Edward Lank is an Assistant Professor at the University of Waterloo and has a PhD from Queen's University.

Venue: This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Summary:

Hypothesis: 

The researchers conducted a study to see what people view as being useful for certain tasks with different motion gestures with a cellphone. Their hypothesis was that by developing a new standard for motion gestures, users will be able to finally use motion gestures in the regular market. 

Methods: 

20 participants, were given tasks developed by the researchers that have a set of motion gestures that are intuitive to certain tasks. By locking a phones screen and not having any feedback from the phone when doing operations, the participants had to do certain actions and movements with the phone to do operations. Example: putting the phone to your face will answer a phone call. After there exercises with the phones, they were given survey's to look at how they were doing. 

Results: 

The  common themes were:

• Mimic Normal Use - A majority of participants preferred natural gestures for common tasks that already involved that motion (putting phone to ear)
• Real-World Metaphors - Gesturing the phone as a non-mobile phone object and using it appropriately (hanging a phone up by turning it face down)
• Natural and Consistent Mappings - doing what users expect (right for one thing and left for the opposite)
• Providing Feedback - confirmation of actions happening and during the action.

 The researchers had found 380 different gestures that were performed in the excercies. They combined all the resulsts into a mapping diagram.  

Conclusion: 

The researchers concluded that there is definite need for followup research, but they believe that they are on  the correct path for developing a standard for intuitive input methods for mobile devices. 

Discussion: 

Their goal was met on setting the foundation of motion gestures for the industry. Some day, someone may actually be able to "text" and drive without it being to dangerous. They wouldn't call it texting then, because most of the gestures would probably be by voice, it sounds exciting anyways. More then likely these will be implemented in the next generation of phones, and will be the standard for all the companies.

Blog #22 Mid-air pan-and-zoom on wall-sized displays

Mid-air pan-and-zoom on wall-sized displays

Authors: Mathieu Nancel, Julie Wagner, Emmanuel Pietriga, Olivier Chapuis, and Wendy Mackay

Authors Bios:

• Mathieu Nancel is a Ph.D. student in Human-Computer Interactions in the insitu team at the University of Paris-Sud.
• Julie Wagner is a Postgraduate Research Assistant at the insitu lab and has a Master's from RWTH Aachen University.
• Emmanuel Pietriga is the interim leader of the insitu lab and has a PhD from Institut National Polytechnique de Grenoble.
• Olivier Chapuis is a team co-head (by interim) of the InSitu research team and has a PhD from the University of Paris VII Diderot.
• Wendy Mackay is a Research Director with INRIA Saclay in France and has a PhD from MIT.

Venue : This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Summary:

Hypothesis: 

The authors claim that wall-sized displays are going to be the future, and yet have no standard for interaction. Their hypothesis is, with a series of possible interaction techniques with display, the future of mid-air interactions will be more effective than traditional hardware. (Better then using a remote!)

Methods:

In order to test their hypothesis, they proposed several possible interactions they narrowed the gestures down to 3 key dimensions: Hands, Gestures, and Degree of Guidance. The degree of guidance dimension has the researchers questioning the right feedback to give users as they interact in a primarily free space. So they tried feedback from using 1D 2D and 3D Input. 

12 unique interaction techniques were used in the study. Which the techniques will come from  linear and circular gestures in both uni-manual and bi manual modes. The researchers expected:

• Two handed gestures will be preferred to one handed gestures and be more accurate
• Linear gestures will be preferred for zooming actions while circular gestures will be preferred for everything else
• 1D and 2D gestures will be faster than 3D gestures
• 1D gestures will be the fastest and 3D gestures will be the most tiring

The 12 participants tested these gestures on a wall-sized display consisting of 32 screens. The participants were also ranked while they were required to navigate and zoom appropriately among a series of circles with some designated as targets that should be focused on.

Results: 

After the experiment, the authors found that the two-handed gesture tasks were performed faster than the one-handed gesture tasks, involving smaller muscle groups for input interactions improves performance and linearly-performed tasks were generally performed faster than circular ones. Circular gestures were slower because it was more often that participants overshot their target with circular gestures than with linear ones. The fastest overall gestures were two-handed linear ones in both the 1D and 2D space.

Conclusion:

In the paper the researchers stated that 1D and 2D spaces performed well in the experiment and shouldn't be forgotten to lower common errors that users performed. The researchers reached their goal and were successful to find gestures that work on these giant displays. And even got surprises in the long run. 

Discussion: 

I found it more isntresting that the hardware based input methods were found to be preferable in many cases, instead of hand gestures. I can't wait to see wall sized displays in my houses, or even in the hall ways in public, it might be a very colorful future for wall displays. 

Blog #20: The aligned rank transform for nonparametric factorial analyses using only anova procedures

The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures

Authors - Jacob O. Wobbrock, Leah Findlater, Darren Gergle, and James J. Higgins

Authors Bios -

• Jacob O. Wobbrock is an Associate Professor at the University of Washington and has a PhD from Carnegie Mellon University.
• Leah Findlater will be an Assistant Professor at the University of Maryland next year, has taught at the University of Washington, and has a PhD from the University of British Columbia.
• Darren Gergle is an Associate Professor at Northwestern University and has a PhD from Carnegie Mellon University.
• James J. Higgins is a Professor of Statistics at Kansas State University and has a PhD from the University of Missouri-Columbia.

Venue - This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

SUMMARY

Hypothesis:
The researchers explain that common procedures used in analyzing non-parametric data tend to be error prone and propose a new system for analyzing data. This new procedure, called the Aligned Rank Transform (ART). The hypothesis is that ART is a better way to analyze data than any other method currently in use in HCI and can be very useful in real world applications

Content:

What ART does is it corrects requirements of ANOVA statistics that are not necessarily ture in HCI. An example of this is normality. To do this, residuals are computed, estimated effects for interactions are computed, aligned responses are computed, average ranks are assigned, ANOVA on this new data is performed, and correctness is check by checking sums.

Methods:

The ART procedure consists of 5 steps: 

1. Computing residuals: for each raw response Y, compute residual = Y - cell mean
2. Computing estimated effects for all main and interaction effects: these are calculated such that Ai is the mean response Yi for rows where factor A is at level i. AiBj is the mean response Yij for rows where factor A is at level i and factor B is at level j. And so on.
3. computing the aligned response Y', assigning average ranks Y'' where Y' = residual + estimated effect.
4. performing a full-factorial ANOVA on Y''

Results:
The results were analyzed using a standard ANOVA first and then an ART to see if any observable interaction concurred in the study. The ANOVA did not detect anything and noted their was no significant interaction but the ART found significant interaction and questioned the findings found earlier which agrees with the researchers initial perception that interaction was obviously present.


Conclusion: The researchers found that ART is useful when analyzing non-parametric data and has been useful in several cases that the researchers had worked on.

Discussion:
This paper was at a very techincal level of understanding. I was fortunate enough to have statistics and at least know what ANOVA was. The researchers achieve their goal of providing a better way to provide to analyze data for HCI research. Its good that they can find anomalies by using ART.

Blog #21: Human model evaluation in interactive supervised learning

 Human model evaluation in interactive supervised learning

Authors : Rebecca Fiebrink, Perry R. Cook, and Daniel Truema


Authors Bios:

• Rebecca Fiebrink:  assistant professor at Princeton University in the department of computer science and music and has a PhD from Princeton.

•  Perry R. Cook : professor emeritus at Princeton University and has a PhD from Stanford. 

• Daniel Truema : music courses at Princeton University and is an accomplished composer and performer on the fiddle and laptop.

Venue : This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Summary

Hypothesis: 

Knowing why machines have certain outputs that are generate are one of many things that one may not get to understand. The researchers proposed a system that allows trainers to supervise machine learning because current ones provide little or no feedback. Which will allow them to find out why some problems may happen and they could fix those problems. They hypothesized that a system with the capabilities of knowing why certain output is generated, will allow us to make better machines. 

Methods: 

The researchers made a tool called, Wekinator, which would record physical gesters using musical intrements and label them. Then the researchers conducted three studies of people applying supervised learning to their work in computer music.

• The first study consisted of 7 composers working to refine the Wekinator to control new instruments that existed on the computer only and responded to gesture input. The participants trained the system once a week and made suggestions that were acted upon in between sessions.

• The second study focused on the supervised learning aspect of the system and observed 21 students in their use of the Wekinator to produce new instruments controlled by certain gestures (one continuously controlled adjusting to changes in gesture in real-time). The students' actions were recorded by the software and they filled out questionnaires.

• The final study consisted of a professional cellist working with the researchers to classify several gestures that capture many properties of a bow as it is used to play the cello. This bow, called the “K-Bow". After the system captures this data, it should be able to capture the notes being played and add them to a composition on a computer.

Results:

In the first study, participants found that the algorithms used to control the sound were difficult to control in a musically satisfying way using either a GUI or an explicitly controlled sequence.

The second study, users in the second study indicated that they considered high levels of cross validation accuracy to be indicative of good performance, and made use of it.

In the Third study, also used high levels of Cross validation. but direct evaluation was used more frequently in all 3 studies and allowed for quick validation that a system was performing as expected.

Concussion:
The researchers presented there work and concluded that supervised learning models intended to be ultimately used in interactive contexts and should be evaluated with attention to the model qualities important to users in those contexts. So basically, users must evaluate what qualities they are looking for beforehand because there simply isn't enough tools to know all.

Discussion:
The researchers did accomplish their goal of gathering useful data regarding evaluation of supervised learning systems, and I think that this work will be very beneficial in the future. Imagine writing a program or operating system that has no bugs!

Blog #19: Reflexivity in digital anthropology

Reflexivity in digital anthropology

Authors : Jennifer A. Rod

Authors Bios: Jennifer A. Rod: Assistant Professor at Drexel's School of Information and has a PhD in Computer Science from UC Irvine.

Venue - This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Summary:

Hypothesis: The paper overviews key aspects its use in HCI, as well as in the anthropological approach. Her hypothesis is that studies can better understand how effectively the use of different types of ethnography can make HCI research  better and easier in the future. 


Methods: Jennifer A. Rod explained 3 different types of ethnography approaches that can be  appled to HCI, although only one is currently used in digital anthropology.

• realist approach: focuses on the process of collecting information over a long period of time and determining causes for what was observed

• confessional approach : relies on the ethnographer explaining their own involvement with the subject being studied and allows for a greater understanding of the perspective being observed.

• impressionistic approach : focuses on telling a story and letting detailed observation speak for itself in setting the stage for the ethnography and allowing for future analysis. 

Using these approaches, and having different points of view in gathering data would allow for the most optimal answers that one might have when doing an ethnography.  

Results: Three types of ethnographies could be created out of the 3 approaches listed above.

• Formative Ethnographies: study current use of a product or tool and make observations as to how it can be improved. 

• Summative Ethnographies: focus purely on how a digital tool is used by a specific group of people and explain this relationship in depth with no suggestions for improvement.

• Iteratively Evaluative Ethnographies: studies that are conducted by putting a prototype product or tool in use and studying how it becomes used, then evaluate it all over again in another study. 

Conclusion:  Dr. Rod concludes the paper by stating that standards need to be adopted for ethnography in HCI in order to continue improving on technology and its relationship with users. By following the different approaches for ethnographies, one would have many different results, and conclude on more answers then needed. 

Discussion:  To better HCI for the future, and to allow user interfaces to become better will be the future of the industry. But my personal opinion, maybe technology shouldn't be taken for granted, and i hope that humans can embraces technology, but shouldn't have there lived ruled by technology. It is good to research technology, better interaction in technology is a good thing, but it may defeat our culture ultimately.

Blog #18: Biofeedback game design: using direct and indirect physiological control to enhance game interaction

Biofeedback game design: using direct physiological control to enhance game interaction
Authors:  Lennart E. Nacke, Michael Kalyn, Calvin Lough, and Regan L. Mandryk

Authors Bios: Lennart E. Nacke was a postdoctoral associate researcher at the University of Saskatchewan at the time of this paper's publication.

Michael Kalyn is a student researcher at the University of Saskatchewan and holds a degree in Computer Engineering.

Calvin Lough is also a researcher working under Dr. Mandryk in the Interaction Lab at the University of Saskatchewan.

Regan L. Mandryk is an Assistant Professor at the University of Saskatchewan and has a PhD from Simon Fraser University.

Venue - This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems.

Summary:

Hypothesis: The researchers explored the physiological input in video games to see if they could be using in the modern video game industry. There hypothesis is that physiological input will be the future of modern gaming. 

Methods: The researchers had build a 2D side scrolling shooter game using Microsoft XNA and the use of a Xbox 360 controller. The game had many physiological controls other then controller input. They added:

• GAZE - Eye tracking
• EMG - Muscle sensor
• GSR - Sweat detection
• EKG - Heart Rate sensor
• RESP - Breathing rate sensor
• TEMP - Temperature sensor (require blowing on)

10 Participants were asked to play through 3 test levels on the video game. The first had a condition where there were no physiological controls other then the Xbox controller (which satisfies as the control of the experiment). The other 2 levels had indirect and direct methods of physiological control. GAZE (which followed where the user was looking on the screen to aim the gun) was used on both test levels. The participants were set to play the levels in random order for 10 minutes at a time, then filled out a survey about their experience with the physiological controls.

Results: The participants found that the physiological controls were significantly more fun then the control level, but had no preference to which level of the game they liked. the participants all liked the GAZE feature and RESP sensors more then the others, so they naturally chose the direct controls over indirect controls. 

Conclusion: The researchers proved there goal that physiological input if feasible to implement into modern games by using more of a direct control format other then indirect, like the EKG monitor. But they do believe that the indirect variables could control how the user is represented or sees the environment they are in while playing a game, ie: if there heart rate jumps up, maybe the screen will start to pulsate and blur a little, giving the sense of one that might be in that situation. 

Discussion: I loved the idea that having a physiological input could be used to enhance game play. One day i would love to play a game that would be more about your physical ability more then how good you can move your figures left and right to determine the best player... Granted that game my lose a lot of popularity because majority of the gaming market i would say in physically weaker. 

Blog #17: Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment

Privacy Risks Emerging from the Adoption of Innocuous Wearable Sensors in the Mobile Environment

Authors: Andrew Raij, Animikh Ghosh, Santosh Kumar, and Mani Srivastava

Authors Bios:
Andrew Raij- a Post Doctoral Fellow in the Wireless Sensors and Mobile Ad Hoc Networks Lab (WiSe MANet) at the University of Memphis and has a PhD from the University of Florida.

Animikh Ghosh - a Junior Research Associate at Infosys Technologies Ltd. and has a Masters of Computer Science from the University of Memphis.

Santosh Kumar - an Associate Professor at the University of Memphis and advises the WiSeMANet Lab.
Mani Srivastava is a Professor of Computer Science at UCLA and is also highly involved as an electrical engineer.

Venue : This paper was presented at the CHI '11 Proceedings of the 2011 annual conference on Human factors in computing systems

Summary


Hypothesis:
The reasearchers of the paper pointed out that mobile sensors that are used to study one's health can also be used for learning new algorithms. Their hypothoses was based on how much individuals care about their private habits being made public; and the researchers believe that people don't want certain personal attributes like health conditions, dictate how people will treat them in the workplace, or use against them.

Methods:
The developers made a framework to better explain the scenario being discussed in the paper and it consisted of

•  measurements - raw data that comes from sensors such as acclerometer readings and heart rates.
•  behavior - actions the user performed that can be inferred from the measurements such as seizures.
•  context - explain behaviors by observing the environment that the behavior occurred in such as time,   place, and people that were nearby.
• restrictions - applied to all 3 of the previous elements and produce a limiting effect such as limiting access to accelerometer data or keeping the time private.
• abstractions  - offer a way to restrict data to a desirable amount so as to have enough data to monitor whatever is being studied but leaving out other details.
• privacy threats - harms that result from matching data to an identity.

The study had 3 goals: assess privacy concern of individuals before and after the study. The researchers used their framework to examine restrictions and abstractions, and assess how identification of the data affects the concern levels of participants.

66 participants were recruited for the study and divided into 2 groups. 1 group, Group NS, only filled out a privacy survey and the other, Group S, gave physiological, behavioral, and psychological data to a sensing device, the AutoSense, for 3 days with questionnaires throughout the study. Upon completion of the data collection participants viewed the data collected in a system called Aha visualization, developed for this study, that showed the data at different abstraction levels and responded to a privacy questionnaire knowing what was collected.

Results: 

Positively-associated activities, like exercise, were acceptable to share, as was location. Group 1 (with no data stake) and the group who had not yet learned of the extent of the data collected about them had similar levels of unconcern for data storage. After learning about their data, the second group had higher concern ratings. The researchers also found that participants seemed initially naive to the danger of shared data, with the exception of location.

Conclusion: 
The researchers wanted to find out how concerned users were about sensor data being used to determine things about them and how to prevent identifiable information from being released. They were correct on their hypothesis that most wouldn't want to give information that could possibly be used to judge them. 

Discussion: 

Since technology is enveloping humans daily lives more and more each year. It's gonna be tough for companies not to notice information, or gain information about somones habits and health condition. I hope in some way, they could use more experiments like this one to gauge what should be private and more protected then other information. I do believe that the researchers found their hypothesis to be valid.