关键词: 加速度传感器, 人体运动模式识别, 多图, 多维标度法, 图嵌入

Abstract: A novel and appropriate algorithm is the key of human activity pattern recognition system. This paper proposes a human activity pattern recognition method utilizing accelerometer data, which adopts a multi-graph embedding representation algorithm for feature dimensionality reduction and a nearest neighbor classifier for pattern classification. Firstly, the proposed method divides the original feature space into several disjoint subsets by clustering so as to generate multiple graphs. Then, the method generates embedding co-ordinates with multidimensional scaling and finds the linear combination of the embedding co-ordinates to represent the original feature space. Finally, the method adopts a nearest neighbor classifier to classify the patterns. The proposed method is novel and simple, and can explore the potential structure of the original feature space with the smallest information loss so that the feature selection is more stable. The experimental results show that the proposed method has a higher accuracy than other representative methods, and can better recognize human activities.

Key words: accelerometer, human activity pattern recognition, multi-graph, multidimensional scaling, graph embedding