A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals

被引:141
作者
Cheng, Juan [1 ]
Chen, Xiang [1 ]
Shen, Minfen [2 ]
机构
[1] Univ Sci & Technol China, Dept Elect Sci & Technol, Hefei 230027, Peoples R China
[2] Shantou Univ, Sch Engn, Shantou 515063, Guangdong, Peoples R China
关键词
Activity awareness; entropy; fall detection; surface electromyography (SEMG); TRIAXIAL ACCELEROMETER; ACTIVITY RECOGNITION; ENTROPY; SYSTEM; SENSOR; CLASSIFIER; HMM;
D O I
10.1109/TITB.2012.2226905
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:38 / 45
页数:8
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