Introduction - If you have any usage issues, please Google them yourself
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.