基于感知-认知解耦的可解释人工智能运动康复评估系统设计与验证

doi:10.3969/j.issn.1006-9771.2026.09.013

Abstract

Abstract:

Objective To propose an X-Gait motor rehabilitation assessment system based on a perception-cognition decoupling architecture to jointly achieve high-accuracy gait recognition and clinical interpretability for low clinical credibility and opaque decision-making workflows in existing intelligent gait assessment models.

Methods In the perception layer, YOLOv11-Pose and PoseFormerV2 were integrated for 2D-to-3D human pose reconstruction. A long short-term memory network was adopted for temporal classification of normal, abnormal and pathological gaits. The layer-wise relevance propagation algorithm was used to identify discriminative joints and automatically quantify biomechanical indices including trunk forward inclination angle, stance phase percentage and stride symmetry. In the cognition layer, rehabilitation-oriented prompt engineering and prior knowledge graphs were combined to drive large language models, converting quantitative biomechanical measurements into clinically reasonable structured reports. Eight subjects were enrolled for bedside clinical validation to evaluate classification performance and clinical consistency.

Results The overall gait classification accuracy of X-Gait reached 94.1%. The 2D pose detection achieved 100% valid detection rate with an average confidence of 0.819. The mean per-joint position error of 3D pose reconstruction was 105.97 mm. In clinical validation of eight cases, outputs generated by explainable artificial intelligence combined with large language models showed high consistency with manual assessments from experienced rehabilitation physicians.

Conclusion The proposed X-Gait system based on perception-cognition decoupling realizes high-precision gait identification and clinically interpretable outputs.

Key words: gait analysis, explainable artificial intelligence, large language model, X-Gait system, 3D human pose estimation, sports rehabilitation

CLC Number:

R496

Guo Xuxia, Yu Hengrui, Lu Yucheng, Yang Sichuang, Long Yu, Li Jiawei, Chen Lin, Nong Feiyu, Long Yaobin. An explainable artificial intelligence-driven motor rehabilitation assessment system based on perception-cognition decoupling: design and validation[J]. Chinese Journal of Rehabilitation Theory and Practice, 2026, 32(9): 1108-1116.

Figures/Tables 10

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References 36

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编号	性别	年龄/岁	原发病	身高/cm	体质量/kg
sub01	男	21	健康	178	75
sub02	男	36	前交叉韧带断裂	172	70
sub03	男	52	右侧股骨骨折	178	70
sub04	女	78	脑出血	160	52
sub05	男	47	脑梗死	173	78
sub06	男	40	脑出血	180	85
sub07	男	52	脑出血	170	76
sub08	男	57	脑梗死	171	74

指标	数值	定性评估
有效检测率	100%	每帧都能成功检测到人体，无漏检，系统鲁棒性强
平均置信度	0.819	> 0.7，可接受，对检测结果有信心
可见关键点数	13.8/17	平均每帧有81%的关节点可见，遮挡较少，输入质量高

指标	总体	正常	异常	病理性
准确率	0.941	-	-	-
精确率	0.948	0.994	0.865	1.000
召回率	0.941	0.886	1.000	0.981
F1分数	0.941	0.937	0.928	0.991
支持数	356	175	128	53

阶段	时间戳	输出
LSTM分类	03:41:02,054	异常，置信度0.974
XAI贡献度分析	03:41:02,054	关键关节：躯干
生物力学特征提取	-	躯干前倾角均值75.4°，标准差3.7°；支撑相占比0.53
语义提示生成	03:41:02,054	模型将该步态分类为异常，置信度97.4%。关键关节为躯干。躯干前倾角度增加70.4°，且支撑相时间缩短至0.53
LLM医学解释生成	03:41:17,762	成功返回结构化自然语言解释

类型	特征描述
单一型	对称步态+单一部位代偿(足弓/躯干)
双维型	对称或轻度不对称+单一平面偏移+支撑延长
复合型	显著不对称+多平面躯干代偿+支撑相严重失衡

An explainable artificial intelligence-driven motor rehabilitation assessment system based on perception-cognition decoupling: design and validation

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编号	主要表现	类型	关键量化指标	医生评估要点	是否一致
sub01	显著不对称，骨盆代偿下降	复合型	躯干前倾24.2°；支撑相占比0.963(左长右短)	右臀中肌无力，Trendelenburg征(+)，支撑时间不等	是
sub02	轻度对称异常，足弓塌陷	单一型	躯干前倾15.9°；支撑相占比0.961；膝内扣0.1°	核心稳定性下降，静态足弓指数低，双侧支撑一致	是
sub03	步长不对称伴支撑期延长	双维型	步长不对称；躯干侧弯6.1°；单侧支撑相明显延长	步长不对称，躯干侧弯5°，单侧支撑相明显延长	是
sub04	步长不对称伴躯干代偿倾斜	双维型	躯干前倾13.5°；躯干侧弯5.4°；单侧支撑相明显延长	步长不对称，躯干前倾10°伴轻度侧弯，支撑相延长	是
sub05	步态对称但躯干侧偏	单一型	躯干前倾9.5°；单侧支撑相明显延长	躯干前倾10°，单侧支撑相代偿性延长	是
sub06	对称步态伴明显侧弯	双维型	步长对称，躯干侧弯8.7°；单侧相支撑明显延长	躯干侧弯10°，单侧支撑相明显延长	是
sub07	不对称步态伴轻度侧弯	双维型	步长不对称；躯干前倾5.1°，侧弯6.3°；单侧支撑相明显延长	步长不对称，躯干侧弯5°及单侧支撑相延长	是
sub08	复合不对称与前倾代偿	复合型	步长不对称；躯干前倾13.3°侧弯6.5°；单侧支撑明显延长	步长不对称，躯干前倾10°，侧弯5°及单侧支撑延长	是

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