According to Hofstede [18], nonverbal behavior is strongly affected by cultural
affordances. The identity dimension, for example, is tightly related to the expression
of emo tions and acceptable emotional displays in a culture, in that, for instance, indi-
vidualistic cultures tolerate individual expressions of anger more easily than do col-
lectivistic cultures. Hofstede et al. [19] explicitly examined the differences that arise
in the use of sound and space for the five dimensions. By relating the results from
their corpus study to Hofstede’s dimensional model, Rehm and colleagues showed
how a user’s expressive gestural behavior can be recognized with high accuracy
and then used to infer the user’s position on Hofstede’s cultural dimensions. With
this contextual information it becomes possible to modify the behavior of an interac-
tive system.
In Part I of this book, vision-based techniques for gesture recognition were pre-
sented in depth. Here the focus is on input techniques that make use of sensoric
equipment to allow more private interactions. Although vision-based techniques
present the most unobstrusive method for movement analysis and have proven very
successful for recognizing gestural activity (perhap s apart from some minor occlu-
sion problems), they may present a severe threat to privacy in ambient intelligence
environments if the user is unaware of the vision device and does not know which
information is being processed, such as affective state, personality traits, or cultural
background. Thus, obstrusive input methods might be more appropriate for sensi-
tive personal information, as they put the control over information transmitted to
the environment into the hands of the user.
In the remainder of this chapter we present input techniques that make use of
acceleration or physiological sensors like EMG. Such techniques rely on sensors that
are small enough to be carried by the user as handheld devices or to be attached to
her body. It is not unreasonable to assume that such sensors will become integrated
into everyday objects like rings or items of clothing, removing this annoyance of
attaching them altogether.
13.6.1 Acceleration-Based Gesture Recognition
With the advent of Nintendo’s new game console, acceleration-based interactions
have become very popular. Although most commercial games seem to rely on rela-
tively primitive information, like raw acceleration, more sophisticated gesture recog-
nition is possible. Schlo¨mer and colleagues [38] made use of HMMs to analyze
acceleration data. They evaluated their approach with an arbitrary set of five ges-
tures and presented user-dependent recognition rates of up to 93% for this five-class
problem. Rehm and colleagues [36] used acceleration-based recognition to capture
340 CHAPTER 13 Nonsymbolic Gestural Interaction for Ambient Intelligence

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