基于多传感器信息融合和迁移学习的下肢外骨骼运动意图预测研究

俞志鹏; 王美玲; 王成军; 凌六一; 金力

doi:10.12263/DZXB.20241134

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基于多传感器信息融合和迁移学习的下肢外骨骼运动意图预测研究

学术论文 | 更新时间：2025-10-16

- 基于多传感器信息融合和迁移学习的下肢外骨骼运动意图预测研究
- Locomotion Intention Prediction via Multi-Sensor Fusion and Transfer Learning for Lower Limb Exoskeletons
- 电子学报 2025年53卷第6期页码：1969-1978
- 作者机构：
  
  1.安徽理工大学人工智能学院，安徽合肥 231131
  2.安徽理工大学煤炭无人化开采数智技术全国重点实验室，安徽淮南 232001
  3.仿生感知与先进机器人技术安徽省重点实验室，安徽合肥 230031
  4.中国科学院合肥物质科学研究院智能机械研究所，安徽合肥 230031
- 作者简介：
  
  [ "俞志鹏男，1995年3月出生于安徽省淮南市.博士，现为安徽理工大学人工智能学院讲师.主要研究方向为人机交互与康复机器人. E-mail: pengyz@aust.edu.cn" ]
  [ "王美玲女，1986年1月出生于安徽省安庆市.博士，现为中国科学院合肥物质科学研究院智能机械研究所副研究员.主要研究方向为机器人与智能装备. E-mail: mlwang@iamt.ac.cn" ]
  [ "凌六一男，1980年7月出生于安徽省枞阳县.博士，现为安徽理工大学人工智能学院教授，博士生导师.主要研究方向为检测技术与智能信息处理. E-mail: lyling@aust.edu.cn" ]
- 基金信息：
  
  安徽理工大学高层次引进人才科研启动基金(2023yjrc111);仿生感知与先进机器人技术重点实验室开放基金(AHFS2024KF06)
- DOI：10.12263/DZXB.20241134
  中图分类号： TP24;TP391
- 收稿：2024-12-18，
  
  修回：2025-05-15，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
俞志鹏, 王美玲, 王成军, 等. 基于多传感器信息融合和迁移学习的下肢外骨骼运动意图预测研究[J]. 电子学报, 2025, 53(06): 1969-1978.

YU Zhi-peng, WANG Mei-ling, WANG Cheng-jun, et al. Locomotion Intention Prediction via Multi-Sensor Fusion and Transfer Learning for Lower Limb Exoskeletons[J]. Acta Electronica Sinica, 2025, 53(06): 1969-1978.
俞志鹏, 王美玲, 王成军, 等. 基于多传感器信息融合和迁移学习的下肢外骨骼运动意图预测研究[J]. 电子学报, 2025, 53(06): 1969-1978. DOI：10.12263/DZXB.20241134

YU Zhi-peng, WANG Mei-ling, WANG Cheng-jun, et al. Locomotion Intention Prediction via Multi-Sensor Fusion and Transfer Learning for Lower Limb Exoskeletons[J]. Acta Electronica Sinica, 2025, 53(06): 1969-1978. DOI：10.12263/DZXB.20241134

摘要

下肢外骨骼需要通过识别穿戴者的运动意图为穿戴者日常活动提供助力，然而当前的研究很少关注能够提供新受试者意图信息的下肢运动模式预测.为此，本文提出了一种基于多传感器信息融合和迁移学习的下肢运动模式预测方法.本文首先设计了一个下肢运动模式预测模型，采用长短时记忆单元（Long-Short Term Memory，LSTM）提取表面肌电信号（surface ElectroMyoGraphy，sEMG）中的模式特征，然后将sEMG的模式特征与关节角度特征融合预测下肢运动模式.考虑到受试者之间的生理信号差异，本文设计的迁移学习策略分两步训练预测模型，第一步在源域受试者数据集上预训练模型，第二步冻结sEMG模式特征提取器的网络权值，并在目标域数据集上微调全连接层.实验采集了受试者自由行走和穿戴外骨骼行走的数据.通过预测时间长度为100 ms的实验可以得出，所提出的方法分别能够有效提升新受试者自由行走状态下和穿戴外骨骼行走时9.53%和8.29%的运动模式预测准确率.实验结果表明，所提出方法可通过提升新受试者运动模式预测准确率，从而保障下肢外骨骼可靠的人体运动意图感知.

Abstract

Lower limb exoskeletons require the capability to identify the user’s lower-limb motion intentions to provide support during daily activities. However

existing research rarely focuses on predicting locomotion modes that provide user intention for new subjects. To bridge this gap

this study proposes a novel method for lower-limb locomotion mode prediction based on multi-sensor signal fusion and transfer learning. The study first designs a prediction model that utilizes long-short term memory (LSTM) networks to extract pattern features from surface electromyography (sEMG) signals. These sEMG features are then fused with joint angle features to predict lower-limb locomotion modes. Considering the inter-subject variability in physiological signals

the method employs a two-step training process using transfer learning. First

the model is pre-trained on a source domain dataset. Next

the weights of the sEMG feature extractor are frozen

and the fully connected layers are fine-tuned using a target domain dataset. Experimental data are collected from subjects performing both normal walking and exoskeleton-wearing walking. Experimental results with a prediction time of 100 ms demonstrate that the proposed method enhances motion pattern prediction accuracy by 9.53% during free walking and by 8.29% during exoskeleton-wearing walking for new subjects. These results suggest that the proposed approach can improve locomotion mode prediction accuracy for new subjects

thereby ensuring reliable human motion intention prediction in lower-limb exoskeletons.

关键词

Keywords

references

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