Page 406 -

396 CHAPTER 13 Measuring the human

confirmed: participants found playing against a friend to be more exciting, and most
had higher GSR and facial EMG levels when playing with a friend. Cardiovascular
and respiratory measures did not show any differences. Investigation of specific
incidents also revealed differences—participants had a greater response to a fight
when playing a friend. Examination of the relationship between GSR, fun, and frus-
tration revealed a positive correlation with fun and a negative correlation with frus-
tration (Mandryk and Inkpen, 2004). The use of multiple coordinated sensors to
measure frustration in game playing continues to be an active area of research, with
more recent papers exploring topics such as the impact of system delays (Taylor
et al., 2015).
EEGs have been also used by HCI researchers to develop brain-computer in-
terfaces that use measurable brain activity to control computers (Millán, 2003).
Machine-learning algorithms applied to EEG signals have been used to distinguish
between different types of activity. Similar to the study of cooperative gaming de-
scribed earlier (Mandryk and Inkpen, 2004), one study found that EEG signals could
be used to distinguish between resting states, solo game play, and playing against an
expert player (Lee and Tan, 2006). Other HCI applications involving EEG signals
include identifying images of interest from a large set (Mathan et al., 2006) and mea-
surement of memory and cognitive load in a military command-and-control environ-
ment (Berka et al., 2004).
Electromyography has been used to measure a variety of emotional responses to
computer interfaces. One study of web surfing tasks found strong correlations be-
tween facial EMG measures of frustration and incorrectly completed tasks or home
pages that required greater effort to navigate (Hazlett, 2003). Similar studies used
EMG to measure emotional responses to videos describing new software features,
tension in using media-player software (Hazlett and Benedek, 2006), and task dif-
ficulty or frustration in word processing (Branco et al., 2005). An experiment involv-
ing boys playing racing games on the Microsoft Xbox established the validity of
facial EMG for distinguishing between positive and negative events (Hazlett, 2006).
Combinations of multiple physiological measures, including EMG, have also been
used to study emotional responses (Mahlke et al., 2006).
A broad body of work has explored the use of body sensing in a variety of
healthcare domains, including assessment of disability, rehabilitation, and in use
by clinicians. Several of these applications have been discussed in this chapter;
for a more in-depth discussion, see “Body Tracking in Healthcare” in O'Hara
et al. (2016).

13.7 SUMMARY

Many HCI questions involve digging deeper than the level of individual tasks. Instead
of simply asking whether a task was completed correctly or how quickly it was com-
pleted, these efforts hope to understand what happened during the completion of the