Motion phase plays an important role in the spatial–temporal parameters of human motion analysis. Multi-sensor fusion technology based on inertial sensors frees the monitoring of the human body phase from space constraints and improves the flexibility of the system. However, human phase segmentation methods usually rely on the determination of the positioning of the sensor and the number of sensors, it is difficult to artificially select the number and position of the sensors, especially when human motion phases are diverse. This paper proposes a selection framework for the sensor combination feature subset for motion phase segmentation, which combines feature selection algorithms with the subsequent classifiers, and determine the optimum combination of the sensor and the feature subset according to the performance of the trained model. Through the constraint and the sensor combination feature subset (SCFS), the filter method can select any number of sensors and control the size of the feature subset; the embedded method can select any number of sensors, but the size of the feature subset is determined by the classifier model. Experimental results show that the proposed framework can effectively select a specified number of sensors without human intervention, and the number of sensors has an impact on the recognition rate of the classifier within 1.5%. In addition, the filter method has good adaptability to a variety of classifiers, and the classifier prediction time can be controlled by setting the subset size of the feature; the embedded method can achieve a better phase segmentation effect than the filter method. For the application of motion phase segmentation, the proposed framework can reliably and quickly identify redundant sensors that provide effective support for reducing the complexity of the wearable sensor system and improving user comfort.

运动阶段在人体运动分析的时空参数中起着重要作用。基于惯性传感器的多传感器融合技术使人体相位的监视不受空间限制，并提高了系统的灵活性。然而，人相分割方法通常依赖于传感器的位置和传感器的数量的确定，因此难以人为地选择传感器的数量和位置，特别是当人的运动相位不同时。本文提出了一种用于运动相位分割的传感器组合特征子集的选择框架，该框架将特征选择算法与后续的分类器结合起来，并根据训练模型的性能确定传感器和特征子集的最佳组合。通过约束和传感器组合特征子集（SCFS），过滤方法可以选择任意数量的传感器，并控制特征子集的大小。嵌入式方法可以选择任意数量的传感器，但是特征子集的大小由分类器模型确定。实验结果表明，所提出的框架可以在没有人工干预的情况下有效地选择指定数量的传感器，并且传感器的数量对分类器的识别率影响在1.5％以内。另外，该滤波方法对各种分类器具有良好的适应性，并且可以通过设置特征的子集大小来控制分类器的预测时间。嵌入式方法比滤波方法具有更好的相位分割效果。对于运动相位分割的应用，