关键词: 柔性传感器, 波形处理, 老年人, 步态分析, 集成学习

Abstract: To further improve the accuracy rate of daily action recognition system for the aged, an action recognition scheme for the aged based on flexible sensor is presented in this thesis. Through the combination of flexible sensor with three dimensional gravity acceleration sensor, the action sequence data collection, processing and recognition method for the aged has been formed. According to the waveform characters of human action sequence, it substitutes the automatic cutting algorithm based on Fourier transform for traditional manual intervened data preprocessing and feature extraction mode; by utilizing Stacking ensemble learning technology, it takes random forest and Naive Bayes as the base classifier and takes logical regression algorithm as secondary classifier to form the classification model. Compared with former traditional technology depending on single three dimensional gravity acceleration sensor and single strong classification model for data collection and analysis, the accuracy rate of recognition of the method proposed in this thesis has been improved significantly and the method has been tested in clinical application. The experimental results show that the action recognition technology for the aged based on flexible sensor and ensemble learning can reach the accuracy rate of above 90% at the time of recognizing various types of action simultaneously.

Key words: flexible sensor, waveform processing, the aged, action analysis, ensemble learning