高可信度加权的多分类器融合行为识别模型

为了提高基于智能移动设备的人体日常行为识别准确率，提出一种高可信度加权的多分类器融合行为识别模型（MCFM）。针对不同智能设备内置加速度传感器获取的三轴加速度信息，优选出与人体行为相关度高的特征集作为该模型的输入，将决策树、支持向量机以及反向传播（BP）神经网络三个基分类器通过高可信度加权投票算（HRWV）法训练出一个新的融合分类器。实验结果表明，所提出的分类器融合模型能有效提高行为识别的准确率，对静止、散步、跑步、上楼及下楼五种日常行为的平均识别准确率达到94.88%。

Multiple classifier fusion model for activity recognition based on high reliability weighted

To improve the recognition accuracy of human activity based on the smart mobile device, an Multiple Classifier Fusion Model for activity recognition (MCFM) based on high reliability weighting was proposed. According to the triaxial acceleration imformation obtained by different smart device with built-in acceleration sensor, those features of high correlation with human daily activities were extracted from the original acceleration as the input of MCFM. Then the three base classifiers of decision tree, Support Vector Machine (SVM) and Back Propagation (BP) neural network were trained for a new fusion classifier by using the High Reliability Weighted Voting (HRWV) algorithm. The experimental results show that the the proposed classifier fusion model can effectively improve the accuracy of human activity recognition, its average recognition accuracy of the five daily activities (stay, walk, run, stairs, downstairs) reaches 94.88%.