人体步态的多源信息融合感知方法研究

doi:10.16182/j.issn1004731x.joss.24-0389

摘要/Abstract

摘要：

为解决下肢外骨骼辅助过程中步态感知能力不足的问题，提出了一种人体下肢步态相位优化分类模型。针对所要采集的步态相位特征信息，设计了一套无线传输步态信息采集系统。利用扩展卡尔曼滤波融合加速度和角速度信息，以准确计算人体关节角度，结合足底压力数据使用核主成分分析法进行降维处理，运用LSSVM对步态数据进行分类并使用PSO寻找最优分类参数。实验结果表明：降维处理后PSO的LSSVM对于步态相位的识别准确率达到了97.4%，优于其他方法，且提高了时效性；验证了使用多源信息融合分类方法对人体步态相位感知的可行性，为下肢外骨骼机器人的助力决策提供支持。

关键词: 多源信息融合, 步态感知, 最小二乘支持向量机, 足底压力, 关节角度

Abstract:

In response to the insufficient gait perception capability during lower limb exoskeleton assistance, a human lower limb gait phase optimization classification model was proposed. A wireless transmission gait information collection system was designed for collecting the required gait phase feature information. Human joint angles were accurately calculated by fusing acceleration and angular velocity information using extended Kalman filtering. Additionally, kernel principal component analysis was applied to reduce dimensionality in conjunction with plantar pressure data. The LSSVM algorithm was employed to classify gait data, and the PSO algorithm was utilized to find the optimal classification parameters. Experimental results demonstrate that after dimensionality reduction, the PSO-based LSSVM outperforms other methods in identifying gait phases and improves timeliness, achieving an accuracy rate of 97.4%. The results validate the feasibility of using a multisource information fusion classification method for human gait phase perception, providing support for the assistance strategy of lower limb exoskeleton robots.

Key words: multisource information fusion, gait perception, least squares support vector machine, plantar pressure, joint angle

中图分类号:

TP391.9

陈贵亮,刘国伟,李永超等 . 人体步态的多源信息融合感知方法研究[J]. 系统仿真学报, 2025, 37(10): 2522-2532.

Chen Guiliang,Liu Guowei,Li Yongchao,et al . Multisource Information Fusion Method for Human Gait Perception[J]. Journal of System Simulation, 2025, 37(10): 2522-2532.

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分类方法	时间
KPCA-PSO-LSSVM	0.001 72
PSOLSSVM	0.008 58
BPNN	0.004 17
RF	0.006 05