Gait Recognition Based on Modified OVR-CSP Fusion Feature and LSTM

This paper proposes a gait recognition method based on the modified OVR-CSP fusion feature of plantar pressure and Long Short-Term Memory classification (referred to as the OVR-CSP-LSTM model). 10 subjects conducted 4 type of gait experiments including normal speed walking, fast walking, slow walking, imitating stroke gait walking in this paper. Transfer the commonly used Common Spatial Pattern (CSP) feature extraction method for EEG to plantar pressure signals, and splice the OVR-CSP features of 2-class, 3-class and 4-class, adopting Long Short Term Memory Network (LSTM) for classification. In this paper, the Intra-patient mode and Inter-patient mode of 10 people are modeled and compared respectively, and the recognition effects under different sensor number and different position sensors' combination are also studied. The experimental results show that the proposed model has good performance for both modes. The method proposed in this article is expected to be applied to multi-sensor signal processing and classification with spatial characteristics.