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378 CHAPTER 13 Measuring the human
computing device in everyday settings (Bernaerts et al., 2014; Quintana et al., 2016).
Exercise and fitness sensors provide similar capabilities—see Chapter 14 for addi-
tional discussion of these sensors.
Microsoft's Kinect takes a different approach to sensing position and mo-
tion. Like the Wii remote, Kinect comes out of the gaming world—in this case,
Microsoft's Xbox. Kinect includes a depth sensor, cameras, and microphones ca-
pable of capture body motion in 3D, and recognizing faces and voices (Zhang,
2012). Kinect sensors have been used in a wide range of contexts, including for
assessing posture and movement (Clark et al., 2012; Dutta, 2012), observing audi-
ence responses to interactive displays (Shi and Alt, 2016), providing feedback to
speakers giving public presentations (Tanveer et al., 2016), interacting with large
displays (Zhang, 2015), and, of course, playing games, both for entertainment
(Marshall et al., 2016; Tang et al., 2015) and for rehabilitation (Huang et al., 2015;
Wang et al., 2014; Muñoz et al., 2014). Data complexity can make analysis of
Kinect interactions somewhat challenging as several types of analyses are needed
to extract objects, human activities, gestures, and even surroundings from Kinect
data (Han et al., 2013). Toolkits such as Kinect Analysis (Nebeling et al., 2015)
might simplify this analysis, but proper design and interpretation will always be a
key component of any study using Kinect or similar data. For a discussion of the
challenges involved in using Kinect data in natural (non-lab) settings, see the LAB-
IN-A-BOX sidebar below.
The Wii, smartphone accelerometers, smart watches, fitness monitors, and Kinect
all provide examples of consumer technologies used in HCI research. These com-
modity tools provide researchers with commercial-quality, ready-to-use hardware
and software that can be readily integrated into research, without requiring any of the
engineering work required to collect data using home-grown or assembled compo-
nents. For further discussion of smart watches and fitness trackers, see Chapter 14.
The need to transcend the limitations of commercial tools has inspired countless
tinkerers and experimenters to develop and adapt novel motion and position sens-
ing tools to both collect input from users and to measure activity. The accessibility
community has been developing novel interfaces enabling users with reduced motor
capacity to control computers since at the 1970s (Meiselwitz et al., 2010). Other
recent efforts have involved the development of any number of innovative sensors.
Fiber optics (Dunne et al., 2006b), flexible sensors (Demmans et al., 2007), and sen-
sors mounted on chairs (Mutlu et al., 2007) have been used to assess posture. Foam
sensors stitched into clothing can detect both respiration and shoulder and arm move-
ments (Dunne et al., 2006a). Wheel rotation sensors' on wheelchairs can be used to
collect motion data suitable for classification of different types of activity (Ding
et al., 2011). One study published in 2015 explored the use of a system for detecting
magnetic radiation from electrical devices. Using an array of sensors worn on a wrist-
band, this system collects and classifies data, identifying electrical devices used by
the wearer (Wang et al., 2015). Although the initial design is often somewhat cumber-
some, these early prototypes pave the way for future refinements that may themselves
lead to commercial innovations. Other efforts might suggest novel uses of existing