用3D步态数据预测多系统健康，突破传统单一病灶思维。
A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion
- 基于3414人数据训练3D骨骼运动基础模型，学习深层特征表示
- 准确预测年龄、体重指数和内脏脂肪面积，相关系数超0.8
- 揭示腿部主导代谢/衰弱，躯干关联睡眠与生活方式，适合健康筛查
步态正被视作重要生命体征，但现有方法多将其视为特定疾病的症状而非全身性生物标志物。我们基于3,414名深度表型成人数据，通过深度摄像头记录五项运动任务的3D骨骼运动，构建了步态基础模型。学习到的嵌入向量优于人工设计特征，可预测年龄（皮尔逊相关系数r=0.69）、BMI（r=0.90）及内脏脂肪组织面积（r=0.82）。该模型显著预测了3,210个表型目标中的1,980个；在调整年龄、BMI、VAT和身高后，步态对男性18个身体系统中的全部、女性17个系统均提供独立预测增益，并提升临床诊断与用药预测能力。解剖消融分析显示，腿部对代谢与虚弱预测主导，躯干则编码睡眠与生活方式表型。这些发现确立步态为独立的多系统生物信号，推动其向消费级视频转化并作为可扩展、被动的生命体征集成。
原文摘要 · Abstract (English)
Gait is increasingly recognized as a vital sign, yet current approaches treat it as a symptom of specific pathologies rather than a systemic biomarker. We developed a gait foundation model for 3D skeletal motion from 3,414 deeply phenotyped adults, recorded via a depth camera during five motor tasks. Learned embeddings outperformed engineered features, predicting age (Pearson r = 0.69), BMI (r = 0.90), and visceral adipose tissue area (r = 0.82). Embeddings significantly predicted 1,980 of 3,210 phenotypic targets; after adjustment for age, BMI, VAT, and height, gait provided independent gains in all 18 body systems in males and 17 of 18 in females, and improved prediction of clinical diagnoses and medication use. Anatomical ablation revealed that legs dominated metabolic and frailty predictions while torso encoded sleep and lifestyle phenotypes. These findings establish gait as an independent multi-system biosignal, motivating translation to consumer-grade video and its integration as a scalable, passive vital sign.
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