| 苏 鸿,马 超,苏 鹏,高经纬.基于 XGBoost 的下肢步态相位识别研究[J].电子测量与仪器学报,2023,37(3):95-101 |
| 基于 XGBoost 的下肢步态相位识别研究 |
| XGBOOST algorithm-based method research on lower limb gait phase recognition |
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| DOI： |
| 中文关键词: 下肢运动姿态 步态相位识别 XGBoost 贝叶斯优化 |
| 英文关键词:lower movement posture gait phase recognition XGBoost Bayesian optimization |
| 基金项目:国家自然科学基金(52005045)项目资助 |
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| 摘要点击次数: 407 |
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| 中文摘要: |
| 针对下肢外骨骼应用中的难点问题,开展了基于 XGBoost 算法,利用单个 IMU 采集的运动姿态数据对步态相位进行识
别的研究。 首先,采集了 6 种不同步态下的足部运动数据,然后将每种步态划分为 4 个相位;在此基础上,以足部运动数据作为
训练集,然后应用 XGBoost 算法进行步态相位识别的分析。 建立模型的过程中通过贝叶斯优化算法进一步对模型中涉及的参
数进行优化。 计算显示,模型的测试集平均正确率为 89. 26%,精度为 89. 64%,召回率为 89. 26%,F1 值为 89. 10%;结果分析表
明该模型能够实现较好的步态相位识别。 |
| 英文摘要: |
| To address the problems in the application of lower limb exoskeleton mechanical equipment, XGBOOT Algorithm-based
research on gait phase recognition is carried out, only using motion attitude data measured by a single IMU. Firstly, foot motion data of
six different gaits are collected, and each gait is divided into four phases. On this basis, XGBOOT algorithm optimized is applied to
analyze the gait phase recognition with the foot motion data as the training set. In the process of establishing the model, the parameters
involved in the model are further optimized by the Bayesian optimization algorithm (BOA). Through calculation, the results show that
the average accuracy of the model is 89. 26% in the verification set, the precision of the model is 89. 64% in the verification set, the
recall rate of the model is 89. 26% in the verification set, F1 value of the model is 89. 10% in the verification set, which indicates that
the model can achieve better gait phase recognition. |
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