基于Tsfresh-RF特征提取的人体步态识别算法

惯性传感器(IMU)由于尺寸小、价格低、精度高以及信息实时性强等优点, 在人体运动信息的获取与控制等方面得到广泛应用, 但在步态识别的时间序列特征提取和步态环境数据等方面还存在着明显的局限. 本文针对人体下肢步态识别特征提取的复杂性及适用性差等问题, 提出基于Tsfresh-RF特征提取的人体步态识别新方法. 首先, 利用IMU获取的人体步态数据集, 构建基于Tsfresh时间序列特征提取和随机森林(RF)的人体步态识别算法模型. 其次, 采用该算法对人体不同传感器位置进行实验, 完成爬梯、行走、转弯等9种人体运动步态的识别. 最后, 实验结果表明所提方法平均分类准确率达到91.0％, 显著高于传统的支持向量机(SVM)与朴素贝叶斯(NB)等方法的识别结果. 此外, 本文所提基于Tsfresh-RF特征提取的人体步态识别算法具有很好的鲁棒性, 将为后续下肢外骨骼机器人的控制提供有利依据.

Human Gait Recognition Algorithm Based on Tsfresh-RF Feature Extraction

Inertial Measurement Unit (IMU) is widely used in the acquisition and control of human motion information due to its small size, low costs, high accuracy, and strong timeliness. However, it still has obvious limitations in the time-series feature extraction and the data about gait environment during gait recognition. Aiming at the complexity and poor applicability of lower-limb gait recognition based on feature extraction, this study proposes a new method of human gait recognition based on Tsfresh-RF feature extraction. Firstly, an algorithm of human gait recognition based on Tsfresh time-series feature extraction and Random Forest (RF) is constructed by a human gait data set acquired by IMU. Secondly, experiments including nine gaits are carried out by this algorithm on different sensor positions, such as climbing, walking, and turning. Finally, the average classification accuracy of the proposed method reaches 91.0%, which is significantly higher than that of traditional Support Vector Machine (SVM) and Naive Bayes (NB) methods. In addition, the proposed algorithm is robust, which will provide a favorable basis for subsequent control of lower-limb exoskeleton robots.