Soft labeling and dynamic weighting prediction for locomotion transition perception in intelligent lower-limb prostheses facing continuous intent
10.3969/j.issn.1006-9771.2026.09.012
- VernacularTitle:面向连续意图的智能下肢假肢运动过渡感知软标签与动态加权预测方法
- Author:
Benyue SU
1
;
Baoqian WANG
1
;
Min SHENG
1
Author Information
1. University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, Anhui 246133, China
- Publication Type:Journal Article
- Keywords:
intelligent lower-limb prosthesis;
continuous locomotion intent;
inertial measurement unit;
dynamic weighting;
mode transition
- From:
Chinese Journal of Rehabilitation Theory and Practice
2026;32(9):1100-1107
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo propose a prediction method combining transition-aware soft labels and dynamic weighting to mitigate transition-zone prediction jitter of hard labels in locomotion intent prediction for intelligent lower-limb prostheses. MethodsSeven healthy subjects were recruited from Tongling University from January to July, 2025, wearing simulated lower-limb-prosthesis braces. Six inertial measurement units were used to acquire acceleration and angular-velocity under 21 locomotion modes. Cosine-based soft labels were introduced to reconstruct the smooth evolutionary gradient of locomotion-mode transitions. Probability-distribution features derived from soft labels were used to build a transition-aware weighting mechanism, which drived the model to focus dynamically on transition boundaries during training. A temporal convolutional network served as the prediction backbone. ResultsThe proposed method achieved an overall prediction accuracy of 94.03% across all locomotion modes, which substantially improved prediction performance during mode transitions, and suppressed probability jitter near decision boundaries and yielded smooth continuous probability trajectories of intent evolution. ConclusionThe proposed transition-aware soft-labeling and dynamic-weighting approach alleviates transition-region prediction oscillation caused by traditional discrete labels. It can provide smooth, robust and anticipatory intent-decision signals for intelligent lower-limb prostheses.
