2.3 Activity Recognition
In ubiquitous computing, one goal of activity recognition is
to provide information that allows a system to best assist
the user with his or her task [20]. Traditionally, activity
recognition research has focused on gait, posture, and
gesture. Bao and Intille used body-worn accelerometers to
detect 20 physical activities, such as cycling, walking, and
scrubbing the floor, under real-world conditions [21]. Logan
et al. studied a wide range of daily activities, such as using a
dishwasher or watching television, using a large variety
and number of ambient sensors, including RFID tags and
infrared motion detectors [22]. Ward et al. investigated the
use of wrist-worn accelerometers and microphones in a
wood workshop to detect activities, such as hammering or
cutting wood [4]. Several researchers investigated the
recognition of reading activity in stationary and mobile
settings using different eye tracking techniques [6], [23].
Our work, however, is the first to describe and apply a
general-purpose architecture for EAR to the problem of
recognizing everyday activities.