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13.3 Motion and position tracking 377

13.3.1 MUSCULAR AND SKELETAL POSITION SENSING
The Wii remote, introduced by Nintendo in 2005, introduced a new era of consumer
electronics capable of sensor position and motion. Using a combination of acceler-
ometers and optical sensing, the Wii remote provides multiple degrees of freedom,
allowing natural inputs for games such as tennis and bowling. In addition to com-
mercial success, the Wii was quickly adopted by HCI researchers who explored the
possibility of enhancing the range of applications to include possibilities such as
gesture recognition (Schlömer et al., 2008), and studied the use and adoption of the
new games, particularly in social contexts (Voida and Greenberg, 2009).
Although the Wii might have been the first notable commercial success, HCI
researchers have been working with novel sensing devices for years. Early published
HCI work with accelerometers predates the Wii by several years (Levin and Yarin,
1999). The use of accelerometers in HCI research exploded with the advent of ubiq-
uitous availability in smartphones. Applications have included sensing posture to
help stroke survivors (Arteaga et al., 2008), identifying repetitive and troublesome
behavior from students with autism spectrum disorder (Albinali et al., 2009), fall
detection (Fudickar et al., 2012; Ren et al., 2012; Mehner et al., 2013), and even de-
tecting bad driving (Singh et al., 2013). Smartphone accelerometers have also been
used as mouse-like input devices (Yun et al., 2015) and for gesture recognition (Kim
et al., 2016).
Moving beyond accelerometers in smartphones, recent years have seen an ex-
plosion in the availability of wrist-worn sensors. Although wrist-watch heart-rate
monitors have been available for years, the current generation of fitness sensors go
much further, adding the capability to track steps, sleep, floor-climbing, and energy
usage, in combination with integrated smartphone functionality. Although concerns
about the accuracy of some measurements may limit the utility of these devices
for some purposes (Kaewkannate and Kim, 2016; Wallen et al., 2016), feedback
provided by these tools may help users understand and increase the efficacy of their
habits. The challenge of understanding how these tools are used over time can be
significant, as technical challenges, nuanced user behavior often involving multiple
devices, accuracy, inappropriate mental models, and other challenges complicate
effective use of the tools and interpretation of resulting data (Harrison et al., 2014;
Rooksby et al., 2014; Yang et al., 2015). As these devices continue to grow in capa-
bility and popularity, further research will undoubtedly continue to ask how these
monitoring capabilities can be used more effectively. For example, one study of
physical activity monitors found that customized plans that encouraged users to
reflect on exercise strategies were more effective than automatically constructed
plans (Lee et al., 2015).
Smartwatches such as the Apple Watch provide wrist-worn easy access to a wider
range of smartphone facilities than those provided by fitness sensors. These watches
have been used to develop approaches for sensing gestures made by fingers (Xu
et al., 2015; Wen et al., 2016; Porzi et al., 2013; Ogata and Imai, 2015). The 2016
example of the Apple Watch presents more opportunities for HCI researchers, par-
ticularly as new tools are developed to explore the use of the watch as an unobtrusive