基于自适应时序对齐的智能下肢假肢运动意图识别

doi:10.3969/j.issn.1006-9771.2025.09.014

《中国康复理论与实践》 ›› 2025, Vol. 31 ›› Issue (9): 1101-1115.doi: 10.3969/j.issn.1006-9771.2025.09.014

基于自适应时序对齐的智能下肢假肢运动意图识别

苏本跃¹^,²^,³(), 刘文瑶²^,³, 宗文杰²^,³, 王保乾²^,³, 盛敏⁴

¹ 铜陵学院数学与计算机学院，安徽铜陵市 244061
² 安庆师范大学计算机与信息学院，安徽安庆市 246133
³ 安徽省铜基材料数字化智能制造工程研究中心，安徽铜陵市 244061
⁴ 安庆师范大学数理学院，安徽安庆市 246133

收稿日期:2025-07-30 修回日期:2025-08-28 出版日期:2025-09-25 发布日期:2025-10-10
通讯作者: 苏本跃，E-mail：subenyue@sohu.com
作者简介:苏本跃(1971-)，男，汉族，安徽芜湖市人，博士，教授，硕士研究生导师，主要研究方向：康复医学、模式识别、人工智能。
基金资助:
1.安徽省高校优秀科研创新团队项目(2023AH010056);2.铜陵学院联合培养硕士研究生创新基金项目(24tlcb01)

Adaptive temporal alignment-based motion intention recognition for intelligent lower-limb prostheses

SU Benyue¹^,²^,³(), LIU Wenyao²^,³, ZONG Wenjie²^,³, WANG Baoqian²^,³, SHENG Min⁴

¹ College of Mathematics and Computer Science, Tongling University, Tongling, Anhui 244061, China
² College of Computer and Information, Anqing Normal University, Anqing, Anhui 246133, China
³ Anhui Engineering Research Center of Intelligent Manufacturing of Copper-based Materials, Tongling, Anhui 244061, China
⁴ College of Mathematics and Physics, Anqing Normal University, Anqing, Anhui 246133, China

Received:2025-07-30 Revised:2025-08-28 Published:2025-09-25 Online:2025-10-10
Contact: SU Benyue, E-mail: subenyue@sohu.com
Supported by:
Excellent Innovative Research Team of Universities in Anhui Province(2023AH010056);Innovation Fund Project for Joint Postgraduate Training of Tongling University(24tlcb01)

摘要/Abstract

摘要：

目的针对智能下肢假肢运动意图识别中因个体步态差异和固定时间窗提取数据导致的运动误分类问题，提出一种基于自适应时序对齐的运动意图识别方法。

方法在下肢运动中，对于连续两个步态周期数据，基于不同稳态模式下的类间差异性，通过跨周期帧间差分检测步态模式一致性。针对检测为单一稳态模式的样本，引入动态时间规整算法将相邻周期运动序列进行对齐，以减小个体差异。提取Haar小波4层分解低频系数构建特征向量，最后通过支持向量机实现分类。试验方案设计为：利用3个惯性测量单元采集测试对象在13种运动模式中下肢的加速度和角速度信息，测试对象为10例健康受试者和1例经胫骨截肢受试者，13种运动模式包括5种稳态模式(平地行走、上楼、下楼、上坡和下坡)和8种转换模式(平地行走与上楼、下楼、上坡、下坡的相互转换)。

结果经过对10例健康人的模拟测试和1例截肢者的测试，5种稳态模式的识别准确率分别为99.24%和100%，13种运动模式的识别准确率分别为98.51%和89.11%。

结论本研究提出了一种自适应时序对齐的运动意图识别方法，该方法有效减小个体步态差异对特征表征的干扰，增强了步态特征的一致性与判别性，最终实现了识别性能的提升。

关键词: 运动意图识别, 智能下肢假肢, 惯性测量单元, 动态时间规整, 自适应时序对齐, 帧间差分, 单一步态模式

Abstract:

Objective To address the issue of motion misclassification caused by individual gait differences and fixed time window data extraction in motion intention recognition for intelligent lower limb prostheses, this study proposes a motion intention recognition method based on adaptive temporal alignment.

Methods In lower limb motion analysis, for continuous gait cycle data, inter-class variability across different steady-state modes was utilized to detect gait pattern consistency through inter-cycle frame differencing. For samples identified as single steady-state modes, the dynamic time warping algorithm was introduced to align adjacent motion sequences, thereby reducing individual variability. Haar wavelet 4-level decomposition was applied to extract low-frequency coefficients for feature vector construction, and classification was performed using a support vector machine. The experimental protocol was designed as follows: three inertial measurement units were used to collect lower limb acceleration and angular velocity data from subjects performing thirteen locomotion modes. The test subjects included ten healthy participants and one transtibial amputee. The locomotion modes consisted of five steady-state modes (level walking, stair ascent, stair descent, ramp ascent, and ramp descent) and eight transition modes (mutual transitions between level walking and stair ascent/descent, as well as ramp ascent/descent).

Results Simulation tests on ten healthy individuals and one amputee showed recognition accuracies of 99.24% and 100% for five steady-state modes, and 98.51% and 89.11% for all thirteen motion modes, respectively.

Conclusion This study proposes an adaptive temporal alignment-based motion intention recognition method. The proposed approach effectively reduces the interference of individual gait variability on feature representation, enhances the consistency and discriminability of gait features, and ultimately improves recognition performance.

Key words: motion intention recognition, intelligent lower-limb prostheses, inertial measurement unit, dynamic time warping, adaptive temporal alignment, inter-frame difference, single gait mode

中图分类号:

R496

苏本跃, 刘文瑶, 宗文杰, 王保乾, 盛敏. 基于自适应时序对齐的智能下肢假肢运动意图识别[J]. 《中国康复理论与实践》, 2025, 31(9): 1101-1115.

SU Benyue, LIU Wenyao, ZONG Wenjie, WANG Baoqian, SHENG Min. Adaptive temporal alignment-based motion intention recognition for intelligent lower-limb prostheses[J]. Chinese Journal of Rehabilitation Theory and Practice, 2025, 31(9): 1101-1115.

图/表 18

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编号	性别	年龄/岁	身高/cm	体质量/kg
HV01	男	24	183	83
HV02	男	27	180	70
HV03	男	26	162	55
HV04	男	27	173	78
HV05	男	30	175	69
HV06	女	26	170	60
HV07	女	24	165	52
HV08	女	24	178	62
HV09	女	24	163	49
HV10	女	18	155	40

模式类别	运动模式	运动模式描述
转换模式	行走—上楼	平地行走到上楼转换
	行走—下楼	平地行走到下楼转换
	行走—上坡	平地行走到上坡转换
	行走—下坡	平地行走到下坡转换
	上楼—行走	上楼到平地行走转换
	下楼—行走	下楼到平地行走转换
	上坡—行走	上坡到平地行走转换
	下坡—行走	下坡到平地行走转换
稳态模式	行走	平地行走
	上楼	稳步上台阶
	下楼	稳步下台阶
	上坡	稳步上坡
	下坡	稳步下坡

数据提取与处理	特征	准确率	精确率	召回率	F1得分
摆动相前45帧	均值方差最值	97.06	98.55	97.42	97.93
摆动相前45帧	小波4层低频系数	97.98	99.02	98.51	98.73
完整摆动相+自适应时序对齐	均值方差最值	98.02	99.89	98.10	98.96
完整摆动相+自适应时序对齐	小波4层低频系数	98.51	99.58	98.31	98.91

分类器	地形识别准确率/%	最终结果
分类器	地形识别准确率/%	准确率/%	精确率/%	召回率/%	F1得分/%	时间消耗/s
LDA	84.74	95.16	99.07	98.08	98.54	0.0291
KNN	93.17	94.32	96.98	95.30	96.05	0.0011
SVM	94.27	98.51	99.58	98.31	98.91	0.0102
RF	90.19	95.49	98.30	98.21	98.20	0.1211
GNB	76.15	77.88	93.76	87.32	90.08	0.1399

插值帧数	准确率/%	精确率/%	召回率/%	F1得分/%	时间消耗/s
16帧	95.45	98.71	98.01	98.33	0.0104
32帧	98.06	98.95	98.86	98.89	0.0140
64帧	98.51	99.58	98.31	98.91	0.0102
128帧	98.19	99.54	98.03	98.75	0.0181
256帧	98.04	99.49	97.91	98.66	0.0353

基于自适应时序对齐的智能下肢假肢运动意图识别

Adaptive temporal alignment-based motion intention recognition for intelligent lower-limb prostheses

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参考文献 40

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文献	传感器		特征	运动模式种类/n		识别精度/%
文献	类型	位置	特征	稳态	转换	识别精度/%
苏本跃等^[33](2020)	IMU	健侧	均值方差最值	5	8	95.10
Sheng等^[38](2021)	IMU	健侧	DTCWT的5层低频系数	5	8	97.27
Liu等^[39](2023)	IMU 地面反作用力鞋垫	健侧	大腿相图形状膝关节角轨迹等	6	-	99.16
Zheng等^[23](2024)	IMU 压力传感器	患侧	大腿倾斜角足部倾斜角步态相位等	7	18	98.04
本研究方法	IMU	健侧	Haar小波的4层低频系数	5	8	98.51

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