一种基于多分类器融合的人体运动行为识别模型

Human Motion Activity Recognition Model Based on Multi-classifier Fusion

为了提高基于智能设备的人体日常行为识别的准确率,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,提出了一种基于多分类器融合的行为识别MCF(Multiple Classifier Fusion)模型。针对5种日常行为(静止、散步、跑步、上楼及下楼),优选出与每种行为相关度高的特征集,用于训练对每种行为识别效果最佳的5个基分类器,并采用一个融合器对5个基分类器的输出进行融合处理,得到最终行为识别结果。该模型对这5种行为的平均识别准确率和可信度分别达到96.84%和97.41%,能有效进行用户行为识别。

To improve the accuracy of human activity recognition based on the triaxial acceleration data from mobile sensors, an activity recognition model based on multiple classifier fusion （MCF） was proposed. The features which are high correlated with each daily activity （staying, walking, running, going upstairs and going downstairs） are extracted from the original acceleration data to generate the five feature data sets to train the five base classifiers. The input of the five base classifiers are these feature data sets, and their output are processed using multi-classifier fusion algorithm to produce the final activity recognition result. The experimental results show that the average activity recognition accuracy and the reliability by using MCF are respectively 96.84% and 97. 41% ,and it can effectively identify human activities.