Description: As an essential branch of context awareness, activity
awareness, especially daily activity monitoring and fall detection,
is important to healthcare for the elderly and patients with chronic
diseases. In this paper, a framework for activity awareness using
surface electromyography and accelerometer (ACC) signals
is proposed. First, histogram negative entropy was employed to
determine the start- and end-points of static and dynamic active
segments. Then, the angle of each ACC axis was calculated to indicate
body postures, which assisted with sorting dynamic activities
into two categories: dynamic gait activities and dynamic transition
ones, by judging whether the pre- and post-postures are both
standing.
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