Daily Behavior Recognition with Single Sensor Based on Functional Time Series Data Modeling
SU Benyue1,2, ZHENG Dandan1,2, SHENG Min2,3
1.School of Computer and Information, Anqing Normal University, Anqing 246133 2.The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133 3.School of Mathematics and Computational Science, Anqing Normal University, Anqing 246133
Abstract：In inertial sensor based human activity recognition, the periodic and temporal characteristics are often ignored in the traditional algorithms, and there are corresponding requirements for the size of the sliding window to extract features. In this paper, a recognition algorithm based on functional data analysis and hidden Markov model for periodic behavior is proposed with a single wearable sensor placed on the waist for human daily activities. Firstly, the functional data analysis method is used to fit the motion capture data of periodic daily activities, and then the single cycle data are extracted after fitting. Secondly,based on the single periodic behavior data, a hidden Markov model describing each daily behavior process is established. Finally, human activities are classified with the maximum likelihood .Compared with the multisensor human activity recognition methods, the proposed method is able to effectively classify 8 daily activities via single sensor with high recognition rates in both user dependent mode and user independent mode.
苏本跃, 郑丹丹, 盛敏. 基于函数型数据时间序列建模的单传感器日常行为识别[J]. 模式识别与人工智能, 2018, 31(7): 653-661.
SU Benyue, ZHENG Dandan, SHENG Min. Daily Behavior Recognition with Single Sensor Based on Functional Time Series Data Modeling. , 2018, 31(7): 653-661.
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2018, Vol. 31 
