基于多示例学习的颈椎健康评分方法

为解决颈椎健康评分及数据标注困难的问题,提出一种基于多值输入多示例学习的颈椎健康状态在线评分算法,仅需在训练阶段对颈椎运动数据长时序列的简单评分标注,即可估计颈椎短时状态的健康评分.首先将多值输入的多示例学习模型划分为多个二值输入的子分类器并分别进行训练,然后使用高斯模型将各个子分类器训练得到的示例分值融合,最后以一种新的包得分机制计算包的分值并完成颈椎健康状态的实时评分.通过包的分值预测准确率计算、示例可视化显示与分析、包得分曲线显示与分析和实时分值评分分析这些定性和定量实验,说明了该算法评估用户颈椎健康的有效性.

Cervical Vertebrae Health Score Method Based on Multiple Instance Learning

This paper introduces an online scoring method for cervical vertebrae health based on multiple instance learning (MIL) of multiple-valued input,in order to assess cervical vertebrae health score and solve the data labeling difficulty.It is only necessary to simply label the long-term sequence of cervical vertebrae motion data during the training phase to estimate the health score of the cervical short-term state.Firstly,the multiple-valued input is divided into sub-classifiers of multiple binary inputs and trained separately.Then use the Gaussian model to fuse the instance scores trained by each sub-classifier.Finally,the bag score is calculated with a new scoring mechanism and the cervical vertebrae health can be assessed in real-time.Qualitative and quantitative experiments include the bag score prediction accuracy,instance visualization analysis,bag score curve analysis and real-time scoring analysis,which illustrate the effectiveness of the algorithm in assessing the health of the cervical vertebrae.