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.

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.