Sensors, Vol. 20, Pages 6055: Empirical Mode Decomposition Based Multi-Modal Activity Recognition

Sensors, Vol. 20, Pages 6055: Empirical Mode Decomposition Based Multi-Modal Activity Recognition Sensors doi: 10.3390/s20216055 Authors: Lingyue Hu Kailong Zhao Xueling Zhou Bingo Wing-Kuen Ling Guozhao Liao This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. T...
Source: Sensors - Category: Biotechnology Authors: Tags: Letter Source Type: research