Non-Contact Human Motion Recognition Based on UWB Radar

Human motion recognition is crucial for surveillance, search and rescue operation, smart homes, and senior care. In daily life, there exists various kinds of human motions with widely different characteristics and meanwhile they also exhibit some clustering features, which make it difficult for recognition. In this paper, a multiple-layer classification method is introduced for comprehensive human motion recognition, including the largest number of motion types ever studied with an ultra-wideband radar system. First, in the pre-screening layer, information in the time-range domain is used to distinguish in situ motions and non-in situ motions. According to different kinds of human motions, the weighted range-time-frequency transform method is proposed to obtain corresponding spectrograms. Then physical empirical features and principal component analysis-based features are extracted for the classifiers to achieve the specific in situ and non-in situ motions, respectively. Extensive experiments have been conducted, achieving the highest accuracy rate of up to 94.4% and 95.3% for in situ motions and non-in situ motions, respectively. The interferences of individual diversity on the proposed method are also investigated. The proposed method could be beneficial in smart homes and senior care.

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