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.