基于旋转模式的移动设备佩戴位置识别方法

移动设备计算能力和传感能力的发展，使其可以为用户提供多种基于情境信息的服务.移动设备的佩戴位置作为一种重要的情境信息，影响着其他用户活动的识别效果和移动应用的自适应功能.分析得出当移动设备在不同身体部位佩戴时，旋转变化模式不同.提出了一种识别设备随身佩戴位置的方法.该方法使用加速计与陀螺仪两种传感器进行数据融合，计算出旋转半径、角速度幅度和重力加速度并提取特征.在分类时，使用随机森林作为分类器，并与使用支持向量机的方案进行了对比.为了检验其效果，在包含3种佩戴位置和13种用户活动种类的公开数据集上进行了实验.实验结果显示，该方法能够达到平均95.39%的交叉验证准确率；同时表明，在满足旋转占主要成分和重力加速度方向稳定的前提下，使用旋转变化信息和集成分类器有助于提高分类效果.与之前的方法相比，该方法可以更准确地对佩戴位置进行区分，并对新用户与新活动类型情况下的位置识别具备更强的泛化能力.

Virtual Resource Evaluation Model Based on Entropy Optimized and Dynamic Weighted in Cloud Computing

The development of the computing power and sensing ability of mobile devices allows them to provide various contextadapted services to users. The on-body position of mobile devices, which is one kind of important context information, affects the recognition of other human activities and the adaptability of many mobile applications. The study provides a method to recognize the on-body positions of mobile devices, inspired by the analysis that different positions on the body have distinguishable rotation patterns. The research then fuses the data sensed by the accelerometer and the gyroscope to calculate the rotation radius, the magnitude of the angular velocity as well as the gravity acceleration and then extract a set of features. A classifier based on the Random Forest is used for classification and compared with the solution based on the support vector machine. To evaluate the method, the paper conducts an experiment on a public dataset with 3 types of positions and 13 types of activities. Results show that the method achieved an accuracy of 95.39% on average in the cross validation and indicate that when rotation is the main component in the movement and the direction of the gravity acceleration is stable, the information about rotation variation and the ensemble classifier are useful to improve the classification accuracy. Compared to previous works, it is able to classify the positions more precisely and has more generalization ability for new users and new activities.