Name
Title | ||
|---|---|---|
A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. |
Abstract | ||
|---|---|---|
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. Next, the dynamic gait activities were identified by the double-stream hidden Markov models. Besides, the dynamic transition activities were distinguished into normal transition activities and falls by resultant ACC amplitude. Finally, a continuous daily activity monitoring and fall detection scheme was performed with the recognition accuracy over 98%, demonstrating the excellent fall detection performance and the great feasibility of the proposed method in daily activities awareness. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TITB.2012.2226905 | IEEE J. Biomedical and Health Informatics |
Keywords | Field | DocType |
mechanoception,static active segment,fall detection scheme,chronic disease,medical signal detection,body posture,patient monitoring,dynamic gait activity,medical signal processing,judging activity,accelerometer signal,double-stream hidden markov model,dynamic transition activity,gait analysis,dynamic active segment,acc axis angle calculation,electromyography,accelerometers,activity awareness,negative entropy,entropy,hidden markov models,fall detection,healthcare,surface electromyography (semg),daily activity awareness,surface electromyography signal,elderly,daily activity monitoring,standing activity,patient | Computer vision,Histogram,Activities of daily living,Pattern recognition,Gait,Remote patient monitoring,Computer science,Accelerometer,Context awareness,Gait analysis,Artificial intelligence,Hidden Markov model | Journal |
Volume | Issue | ISSN |
17 | 1 | 2168-2208 |
Citations | PageRank | References |
12 | 0.69 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Cheng | 1 | 62 | 11.53 |
| Xiang Chen | 2 | 139 | 30.34 |
| M. Shen | 3 | 45 | 12.04 |