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

doi:10.3969/j.issn.1006-9771.2026.09.012

Abstract

Abstract:

Objective To 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.

Methods Seven 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.

Results The 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.

Conclusion The 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.

Key words: intelligent lower-limb prosthesis, continuous locomotion intent, inertial measurement unit, dynamic weighting, mode transition

CLC Number:

R496

Su Benyue, Wang Baoqian, Sheng Min. Soft labeling and dynamic weighting prediction for locomotion transition perception in intelligent lower-limb prostheses facing continuous intent[J]. Chinese Journal of Rehabilitation Theory and Practice, 2026, 32(9): 1100-1107.

Figures/Tables 6

Table 1

Table 2

Table 3

Table 4

Figure 1

Figure 2

References 33

[1]	Sullivan L M, Cowan M, Gabert L, et al. Unified control of a powered knee-ankle prosthesis enables walking, stairs, transitions, and other daily ambulation activities[J]. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 3024-3039. doi: 10.1109/TNSRE.2025.3595496
[2]	黄品高. 智能下肢假肢运动意图的感测与识别关键技术研究[D]. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2020.
	Huang P G. Study on the key technologies of motion intention sensing and recognition of intelligent lower limb prostheses[D]. Shenzhen: The University of Chinese Academy of Sciences (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences), 2020.
[3]	唐易, 陈奕希, 喻洪流, 等. 一种面向下肢假肢的运动意图识别方法及验证[J]. 信息与控制, 2023, 52(5): 598-606.
	Tang Y, Chen Y X, Yu H L, et al. Research and verification of a motion intention recognition method for lower limb prosthesis[J]. Inform Control, 2023, 52(5): 598-606.
[4]	顾洪, 李伟达, 李娟. 智能膝关节假肢研究现状及发展趋势[J]. 中国康复理论与实践, 2016, 22(9): 1080-1085.
	Gu H, Li W D, Li J. State-of-the-art and development of intelligent knee prosthesis (review)[J]. Chin J Rehabil Theory Pract, 2016, 22(9): 1080-1085.
[5]	王启宁, 郑恩昊, 陈保君, 等. 面向人机融合的智能动力下肢假肢研究现状与挑战[J]. 自动化学报, 2016, 42(12): 1780-1793.
	Wang Q N, Zheng E H, Chen B J, et al. Recent progress and challenges of robotic lower-limb prostheses for human-robot integration[J]. Acta Automat Sinic, 2016, 42(12): 1780-1793.
[6]	Tang C Y, Xu Z Y, Occhipinti E, et al. From brain to movement: wearables-based motion intention prediction across the human nervous system[J]. Nano Energy, 2023, 115: 108712. doi: 10.1016/j.nanoen.2023.108712
[7]	张意彬, 吕杰, 喻洪流. 基于模糊逻辑算法的智能膝关节假肢步态相位识别[J]. 中国康复理论与实践, 2023, 29(8): 896-902. doi: 10.3969/j.issn.1006-9771.2023.08.005
	Zhang Y B, Lü J, Yu H L. Gait phase recognition in intelligent above-knee prosthesis based on fuzzy logic algorithm[J]. Chin J Rehabil Theory Pract, 2023, 29(8): 896-902.
[8]	孙为双, 路知远, 公维军, 等. 表面肌电信号在脑卒中手功能康复机器人中的研究进展[J]. 中国康复医学杂志, 2025, 40(5): 790-794.
[9]	张尧. 智能动力膝关节假肢的人体运动意图识别技术研究[D]. 长春: 吉林大学, 2024.
	Zhang Y. Research on human locomotion intent recognition technology for intelligent powered knee prosthesis[D]. Changchun: Jilin University, 2024.
[10]	Li J, Gu X, Qiu S, et al. A survey of wearable lower extremity neurorehabilitation exoskeleton: sensing, gait dynamics, and human-robot collaboration[J]. IEEE Trans Syst Man Cybernetics Syst, 2024, 54(6): 3675-3693.
[11]	杨平, 陈浩源, 高睿馨, 等. 基于低成本惯性测量单元和力敏电阻的足部三维运动数据建模[J]. 中国康复理论与实践, 2024, 30(10): 1224-1231. doi: 10.3969/j.issn.1006-9771.2024.10.013
	Yang P, Chen H Y, Gao R X, et al. Three-dimensional modeling of foot motion based on low-cost inertial measurement unit and force sensing resistor[J]. Chin J Rehabil Theory Pract, 2024, 30(10): 1224-1231.
[12]	Wang E, Chen X, Li Y, et al. Lower limb motion intent recognition based on sensor fusion and fuzzy multitask learning[J]. IEEE Trans Fuzzy Syst, 2024, 32(5): 2903-2914. doi: 10.1109/TFUZZ.2024.3364382
[13]	Su B Y, Wang J, Liu S Q, et al. A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis[J]. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(5): 1032-1042. doi: 10.1109/TNSRE.7333
[14]	盛敏, 夏安琦, 王可林, 等. 基于几何与物理特征融合的智能下肢假肢运动意图识别[J]. 控制与决策, 2022, 37(4): 953-961.
	Sheng M, Xia A Q, Wang K L, et al. Movement intention recognition of intelligent lower limb prosthesis based on the fusion of geometric and physical features[J]. Control Decision, 2022, 37(4): 953-961.
[15]	盛敏, 刘双庆, 王婕, 等. 基于GMM-HMM模型的智能下肢假肢运动意图识别[J]. 仪器仪表学报, 2019, 40(5): 169-178.
	Sheng M, Liu S Q, Wang J, et al. Motion intent recognition of intelligent lower limb prosthesis based on GMM-HMM[J]. Chin J Sci Instrument, 2019, 40(5): 169-178.
[16]	苏本跃, 张利, 何清旋, 等. 基于小波特征匹配的短时人体行为识别[J]. 系统仿真学报, 2023, 35(1): 158-168. doi: 10.16182/j.issn1004731x.joss.22-0176
	Su B Y, Zhang L, He Q X, et al. Short-time human activity recognition based on wavelet features matching[J]. J Syst Simulat, 2023, 35(1): 158-168.
[17]	苏本跃, 王婕, 刘双庆, 等. 惯性动捕数据驱动下的智能下肢假肢运动意图识别方法[J]. 自动化学报, 2020, 46(7): 1517-1530.
	Su B Y, Wang J, Liu S Q, et al. An improved motion intent recognition method for intelligent lower limb prosthesis driven by inertial motion capture data[J]. Acta Automat Sinic, 2020, 46(7): 1517-1530.
[18]	苏本跃, 宗文杰, 刘文瑶, 等. 基于足底动力相数据和小波变换自适应分解的下肢意图识别方法[J]. 控制与决策, 2025, 40(10): 3005-3018.
	Su B Y, Zong W J, Liu W Y, et al. Lower limb prosthesis intention recognition method based on powered plantarflexion phase data and wavelet transform adaptive decomposition[J]. Control Decision, 2025, 40(10): 3005-3018.
[19]	苏本跃, 刘文瑶, 宗文杰, 等. 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. doi: 10.3969/j.issn.1006-9771.2025.09.014
	Su B Y, Liu W Y, Zong W J, et al. Adaptive temporal alignment-based motion intention recognition for intelligent lower-limb prostheses[J]. Chin J Rehabil Theory Pract, 2025, 31(9): 1101-1115.
[20]	Cheng S H, Laubscher C A, Gregg R D, et al. Controlling powered prosthesis kinematics over continuous inter-leg transitions between walking and stair ascent/descent[J]. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 3891-3901. doi: 10.1109/TNSRE.2024.3485643
[21]	Ma X, Liu Y, Zhang X, et al. Real-time continuous locomotion mode recognition and transition prediction for human with lower limb exoskeleton[J]. IEEE J Biomed Health Inform, 2025, 29(2): 1074-1086. doi: 10.1109/JBHI.2024.3462826
[22]	Xue D F, Wang Q N. Noninvasive human-prosthesis interfaces for locomotion intent recognition: a review[J]. Cyborg Bionic Syst, 2021, 2021: 9863761.
[23]	Batista P G, Vieira A C, Gaspar P D. Robotic prostheses and neuromuscular interfaces: a review of design and technological trends[J]. Machines, 2025, 13(9): 804. doi: 10.3390/machines13090804
[24]	Wu J, Maurenbrecher H, Schaer A, et al. Human gait-labeling uncertainty and a hybrid model for gait segmentation[J]. Front Neurosci, 2022, 16: 976594. doi: 10.3389/fnins.2022.976594
[25]	Haque M R, Islam M R, Sazonov E, et al. Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control[J]. Front Robot AI, 2024, 11: 1267072. doi: 10.3389/frobt.2024.1267072
[26]	Zheng E, Wan J, Gao S, et al. Adaptive locomotion transition recognition with wearable sensors for lower limb robotic prosthesis[J]. IEEE/ASME Trans Mechatron, 2024, 29(5): 3481-3492.
[27]	Chen Z H, Zhang T Y. Evaluation of basic sports actions for students based on DTW posture matching algorithm[J]. Syst Soft Comput, 2025, 7: 200196.
[28]	Wang X M, Bai S P, Li L R, et al. Enhancing locomotion-mode recognition and transition prediction with (bio)mechanical sensor fusion for intelligent prosthetic knees[J]. IEEE J Biomed Health Inform, 2025, 29(11): 7955-7968. doi: 10.1109/JBHI.2025.3583319
[29]	Huang H, Zhang F, Hargrove L J, et al. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion[J]. IEEE Trans Biomed Eng, 2011, 58(10): 2867-2875. doi: 10.1109/TBME.2011.2161671 pmid: 21768042
[30]	Young A J, Simon A M, Fey N P, et al. Intent recognition in a powered lower limb prosthesis using time history information[J]. Ann Biomed Eng, 2014, 42(3): 631-641. doi: 10.1007/s10439-013-0909-0 pmid: 24052324
[31]	杜妍辰, 杨良栋, 汪晓铭, 等. 基于肌压信号与惯性测量单元的下肢运动意图识别[J]. 信息与控制, 2024, 53(6): 761-773.
	Du Y C, Yang L D, Wang X M, et al. Recognition of lower limb movement intention based on force myography signal and inertial measurement unit[J]. Inform Control, 2024, 53(6): 761-773.
[32]	Liu Y X, Gutierrez-Farewik E M. Joint kinematics, kinetics and muscle synergy patterns during transitions between locomotion modes[J]. IEEE Trans Biomed Eng, 2023, 70(3): 1062-1071. doi: 10.1109/TBME.2022.3208381
[33]	Hu N H, Englebienne G, Lou Z Y, et al. Learning to recognize human activities using soft labels[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(10): 1973-1984. doi: 10.1109/TPAMI.2016.2621761

编号	性别	年龄/岁	身高/cm	体质量/kg
Sub01	男	24	183	83
Sub02	男	27	180	70
Sub03	男	26	180	85
Sub04	男	30	172	65
Sub05	女	22	172	59
Sub06	女	26	161	60
Sub07	女	25	165	48

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—上坡	平地行走到上坡转换
	行走—下楼	平地行走到下楼转换
	行走—下坡	平地行走到下坡转换
	行走—站立	平地行走到站立转换
	上楼—行走	上楼到平地行走转换
	上坡—行走	上坡到平地行走转换
	下楼—行走	下楼到平地行走转换
	下坡—行走	下坡到平地行走转换
	站立—行走	站立到平地行走转换
	坐—站立	坐到站立转换
	站立—坐	站立到坐转换
稳态模式	行走	平地行走
	上楼	稳步上台阶
	上坡	稳步上坡
	下楼	稳步下台阶
	下坡	稳步下坡
	右转弯	平地右转弯
	左转弯	平地左转弯
	坐	保持坐姿
	站立	保持站立

方法	准确率/%	加权F1/%	过渡帧F1/%	过渡帧MSE
硬标签CE	93.66±0.73	93.47±0.76	—	—
硬标签CE+TC	93.94±0.65	93.72±0.71	—	—
软标签CE (γ = 0)	93.89±0.64	93.65±0.67	78.17±2.10	0.00790
软标签CE+TAW (γ = 2)	94.03±0.66	93.81±0.69	78.71±1.82	0.00755
MSE (γ = 0)	93.99±0.60	93.77±0.63	77.95±1.38	0.00789
MSE+TAW (γ = 2)	94.03±0.58	93.80±0.60	78.40±1.64	0.00742

方法	稳态帧准确率	过渡帧准确率	稳态帧F1	过渡帧F1
软标签CE (γ = 0)	96.70	78.64	96.53	78.17
软标签CE+TAW (γ = 2)	96.76	79.13	96.61	78.71
MSE (γ = 0)	96.88	78.40	96.72	77.95
MSE+TAW (γ = 2)	96.81	78.89	96.66	78.40

Soft labeling and dynamic weighting prediction for locomotion transition perception in intelligent lower-limb prostheses facing continuous intent

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 33

Related Articles 3

Metrics

Comments

Recommended 0

[1]	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.
[2]	YANG Ping, CHEN Haoyuan, GAO Ruixin, WANG Xinping, Philip ROWE. Three-dimensional modeling of foot motion based on low-cost inertial measurement unit and force sensing resistor [J]. Chinese Journal of Rehabilitation Theory and Practice, 2024, 30(10): 1224-1231.
[3]	YANG Bo;FU Cheng-long. Inertial Measurement Unit and Fall Risk Assessment in the Elderly (review) [J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2015, 21(07): 780-784.