基于可穿戴惯性传感技术的人体步态阶段识别

Human gait phase recognition based on wearable inertial sensing technology

为了实现基于可穿戴惯性传感技术的人体步态阶段识别,开发了基于特征选择的人体步态阶段识别模型、基于时间比例优化的人体步态阶段识别模型和基于机器学习多数据类型、多特征、多分类器的人体步态阶段识别模型,并对比了3种模型的步态阶段识别效果。结果表明:基于特征选择的人体步态阶段识别模型的平均识别准确率为73.66%;基于时间比例优化的人体步态阶段识别模型的平均识别准确率为90.96%;利用脚背处俯仰角数据和加速度数据训练得到的基于机器学习的人体步态阶段识别模型的平均识别准确率分别为97.04%、86.80%;针对不同的步态阶段和使用场景,可差异化选择不同的识别方法以获得理想的识别效果;综合采用时间比例优化算法和机器学习方法可以获得较高的综合识别准确率。该研究可为进一步开展基于可穿戴式传感器的人体行为相关研究提供参考。

In order to realize recognition of human gait phases based on wearable inertial sensing technology,the human gait phase recognition models based on feature selection,time proportion optimization,and machine learning with multiple data types,multiple features,and multiple classifiers were developed to recognize the human gait phases,and the recognition effect of the three models are compared.The results show that the average accuracy of human gait phase recognition based on feature selection is 73.66%,on time proportion optimization is 90.96%,and on machine learning models trained with pedal pitch angle data and acceleration data is 97.04%and 86.80%,respectively.Different recognition methods can be selectively used according to different human gait phases and application scenarios to achieve desired recognition effects.The comprehensive use of time proportion optimization algorithm and machine learning methods can achieve high comprehensive recognition accuracy.The paper provides a reference for further research on human behavior based on wearable sensors.