Adaptive temporal alignment-based motion intention recognition for intelligent lower-limb prostheses
10.3969/j.issn.1006-9771.2025.09.014
- VernacularTitle:基于自适应时序对齐的智能下肢假肢运动意图识别
- Author:
Benyue SU
1
;
Wenyao LIU
;
Wenjie ZONG
;
Baoqian WANG
;
Min SHENG
Author Information
1. 铜陵学院数学与计算机学院,安徽 铜陵市 244061;安庆师范大学计算机与信息学院,安徽 安庆市 246133;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
- Publication Type:Journal Article
- Keywords:
motion intention recognition;
intelligent lower-limb prostheses;
inertial measurement unit;
dynamic time warping;
adaptive temporal alignment;
inter-frame difference;
single gait mode
- From:
Chinese Journal of Rehabilitation Theory and Practice
2025;31(9):1101-1115
- CountryChina
- Language:Chinese
-
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 mo-tion sequences,thereby reducing individual variability.Haar wavelet 4-level decomposition was applied to ex-tract 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/de-scent).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 pro-posed approach effectively reduces the interference of individual gait variability on feature representation,en-hances the consistency and discriminability of gait features,and ultimately improves recognition performance.
