Abstract
Inertial sensors embedded in commercial smartwatches and fitness bands are among the most informative and valuable on-body sensors for monitoring human behavior. This is because humans perform a variety of daily activities that impacts their health, and many of these activities involve using hands and have some characteristic hand gesture associated with it. For example, activities like eating food or smoking a cigarette require the direct use of hands and have a set of distinct hand gesture characteristics. However, recognizing these behaviors is a challenging task because the hand gestures associated with these activities occur only sporadically over the course of a day, and need to be separated from a large number of irrelevant hand gestures. In this chapter, we will look at approaches designed to detect behaviors involving sporadic hand gestures. These approaches involve two main stages: (1) spotting the relevant hand gestures in a continuous stream of sensor data, and (2) recognizing the high-level activity from the sequence of recognized hand gestures. We will describe and discuss the various categories of approaches used for each of these two stages, and conclude with a discussion about open questions that remain to be addressed.
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Parate, A., Ganesan, D. (2017). Detecting Eating and Smoking Behaviors Using Smartwatches. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_10
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DOI: https://doi.org/10.1007/978-3-319-51394-2_10
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