©2010 john wiley and sons chapter 13 research methods in human-computer interaction chapter 13...

©2010 John Wiley and Sons www.wileyeurope.com/college/lazar Chapter 13

Research Methods inHuman-Computer Interaction

Chapter 13

Measuring the Human


Participant as data source

• Physical and emotional measurements can give significant into how we use computers

• Possibly data that would be hard to get in other ways

• Many approaches• Range of cost, complexity, and

invasiveness


Examples

• Mouse/keyboard• Eye-tracking• Measurements of physical and

emotional responses– Galvanic skin response, blood volume,

heart rate measurement

• EKG/EMG• Brain scan? FMRI• Cost-benefit tradeoffs


Eye-Tracking

• Where does the user look? • Where does she expect to find data?• When is she lost/confused? • Cameras or other sensors track position

or orientation of eyes or other parts of body

• Transform raw data into detailed descriptions of “paths” of eye gaze


Eye-Gaze Challenges

• Eyes constantly in motion– Rapid eye motions – saccades – help us

focus– Larger motions indicate change of focus

• “Dwell” - relatively little motion indicating focus on a target– Thresholds for identifying a “dwell”?

• Can be applied at larger scale


Head tracking for large displays and virtual environments

Ball, North, & Bowman, 2007


Challenges

• Expense of equipment• Low-cost alternatives based on USB

webcams may be possible• Hard to interpret

– Where you are looking is useful– But it must be coordinated with what you

are looking at

• Overlay trail on screen shot indicating path of user gaze


Web Page with Eye-tracking trails

Card, et al, 2001


Uses for eye-gaze

• Pointing and selecting– Assistive technology

• Impact of interface design– Placement of target in list of links– Length of text summaries for search

results

• Eye movement during menu selection– Fixation vs. movements in specific

directions - “sweeps”


Dimensions of Eye-Tracking Designs

• Hypothesis-driven experiments– Well-defined tasks, measures, etc.– Do users look at security indicators on

web browsers? (Whalen & Inkpen, 2005)

• Exploratory, open-ended research– “Let's see what they look at”?


Physiological Tools

• Bodies change behavior with stimuli• Measurable differences when we are

– Excited– Frustrated– Aroused

• Measurements from bodies can be used to understand these responses


Psychophysiology

• Combination of physiological signals with traditional study of task performance and subjective responses

• Goal: get fine-grained assessments of subjective responses– As they happen, rather than later (via

surveys)– Replace recall with measurement,

subjective response with quantitative measurement


Types of Physiological Data

• Current flow – every cell is an electrical system

• Blood flow• Heart Rate• Respiration Rate


Two Classes of Sensors

• Electrodes– Direct measurements of electrical

signals– Galvanic skin response

• Transducers– Convert mechanical or physical

measurements to electrical form– Pressure sensors to measure posture


Challenges

• No single notion of arousal• Stimulus-Response specificity

– Certain stimuli may lead to increases in some measures alongside decreases in others

• Need to understand the range of potential responses that might be associated with stimuli used in your tasks.


Where are measurements made?

• Electrodermal Activity/Galvanic Skin Response – fingers/toes

• Blood volume pressure – finger

• Electrocardiography (EKG) – chest/abdomen

• Respiration – Thorax

• Muscular/Skeletal Positioning – Varied

• Electromyography (EMG) – Jaw/Face

• Electroencephalography (EKG)/Evoked responses - head


Electrodermal Activity/Galvanic Skin Response (GSR)

• Measurement of flow of electricity through the skin

• Pair of electrodes on fingers• Conductance levels linked to fear,

sadness, arousal, cognitive activity, and frustration


Galvanic Skin Response (GSR) Sensors

• Thought Technology (http://www.thoughttechnology.com/sensors.htm)


Cardiovascular

• Stimuli may cause complex changes in heart behavior– Variability in heart rate, blood pressure,

and blood-volume pressure

• Used to measure stress, mental effort, fear, happiness, and anger


Blood Volume Pressure (BVP)

• Light-absorption properties of capillaries in finger

• Changes in pressure lead to changes in reflected light

• Correlate with stimuli that provoke anxiety

• Also can be used to infer changes in heart rate


Electrocardiography (EKG)

• Source: electrical current that causes heart to beat

• Measure– Heart rate– Interval between beats– Heart-rate variability


Respiration

• Arousal makes us breather faster• Some emotions can cause irregular

breathing

• Use sensors attached to thorax or integrated into clothing

• Measure expansion and contraction of chest


Muscular and Skeletal Position Sensing

• How do we sit? How do we move?• Various sensors measure different types

of movement and positioning– Fiber optics– Accelerometers– Computer vision– Foam sensors in clothing– Pressure Sensors

• Nintendo Wii..


Electromyography (EMG)

• Measure muscle tension

• Measurements on jaw – Tensions associated with clenching

• Eyebrows or cheeks– Frowns or smiles– Mild positive emotions lead to lower

readings over the eyebrow and higher over cheek

• Also sadness, fear, happiness


Brain Activity

• Brain imaging – detailed displays, but expensive

• Electroencephalography (EEG) – Helmet with electrodes on scalp

• Functional MRI (fMRI) – identify regions of the brain activated in response to specific stimuli

• Functional near-infrared spectroscopy (fNIRS) – measure reflective characteristics of skull, scalp, and brain)– Measure cognitive load in tasks?


Data Collection Challenges

• Electrodes and sensors can be difficult to use correctly– Get proper training– Work with an experienced professional

• May cause some discomfort and unease– Electrodes should only be attached by

someone of the same gender?– Be explicit in informed consent (Chapter

14)


More Data Collection Challenges

• Electromyography needles placed in skin– Safe, but “Don't try this at home”– Use electrodes placed on skin instead


Data Interpretation Challenges

• Signals are very noisy– Distortion– Interference

• Comparison to individual “baseline” measurements necessary

• Magnitude of responses may be influenced by baseline– Small increase in heart beat if heart is

already beating quickly


More Interpretation Challenges

• Habituation – response to stimulus decreases after repeated presentation

• Use signal processing techniques to filter and “clean” data– But, you need to know how to use them

• Data granularity – Averages OK for overall impressions– Specific responses require synchronize

measurement stream with user actions


Synchronization Challenges

• Multiple data streams• Actions on the computer (log file)• Timing of physiological measurements

– Often collected on a second computer..

• Modified mouse with signals to two computers?


Example Experimental setup

• Two computers: one for stimulus, one for recording physiological measurements

• Mouse with two outputs• Additional display for clock• Video camera• Blood Volume Pressure and Galvanic

Skin Response sensors• Scheirer, et al., 2002


Example Experimental Setup

• Scheirer, et al., 2002


Analysis Challenges

• Modeling to classify types of responses?

• Determining the emotional state associated with a response?– Happiness? Sadness? Disgust? Fear?

• Data mapping from measured signals to emotional states is inconclusive

• Mixed or incomplete signals?


Design Questions?

• Do the benefits of physiological measurement outweigh the costs?

• Would easier methods (post-task questionnaires & observations) provide data that is almost as good, at a lower cost?


Example: Multi-modal Input

• Galvanic Skin Response (GSR) to test emotional response to speech, gesture, and multimodal interfaces

• Response levels lowest for multimodal, then speech, gestures

• Total response increases with task complexity

• GSR peaks correlated with stressful or frustrating events

(Shi, et al. 2007)


Example: Video Games

• GSR, EKG, cardiovascular rate, respiration, facial EMG

• Measure responses to computer games played against computer and against a friend

• Playing against a friend was more exciting– Most had higher GSR and facial EMG– No differences on cardiovascular and

respiratory measures

(Mandryk & Inkpen, 2004)


End-of-chapter

• Summary

• Discussion questions

• Research design exercise

©2010 john wiley and sons chapter 13 research methods in human-computer interaction chapter 13...

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