Research on Fall Detection Classification Recognition Based on Extreme Learning Machine
1 SinoDutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China
2 School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Abstract：Falls in the elderly population is a very common phenomenon because of the limitation of age and physical condition. Therefore, it is important to remote monitor the elders state real time according to the movement characteristics of falling down, in order to take actions in time when elderly people fall down in some emergency situations. Characteristics of human body kinematics were analyzed in view of the human movement state monitoring, and an algorithm that fall detection based on extreme learning machine (ELM) was proposed. The method is to collect the body’s 3D acceleration value through a sensor. Then the model of fall detection was established. On the basis of the model, a fall detection classifier based on ELM was established and the computer aided detection for falls was completed. The 540 samples of experimental data were obtained, 440 samples for training and 100 samples for testing. The accuracy of the method was 93%, the sensitivity was 87.5%, and the specificity was 91.7%. Results show that the established method has good classification performance and advantages on the running time for classification of training in reference to the conventional machine learning.
王之琼1,2曲璐渲1 隋雨彤1 . 基于极限学习机的跌倒检测分类识别研究[J]. 中国生物医学工程学报, 2014, 33(4): 418-424.
WANG Zhi Qiong1,2QU Lu Xuan1 SUI Yu Tong1 BAO Nan1 KANG Yan1#*. Research on Fall Detection Classification Recognition Based on Extreme Learning Machine. journal1, 2014, 33(4): 418-424.
