用智能手表数据自动识别肥胖相关行为，准确率达96.86%。
COBRA: Multimodal Sensing Deep Learning Framework for Remote Chronic Obesity Management via Wrist-Worn Activity Monitoring
- 融合空间与时间特征的深度网络，识别四种肥胖相关行为。
- 在51人数据集上达到96.86%准确率，最高分类精度超98%。
- 模型对人群差异不敏感，适合长期个性化健康管理。
慢性肥胖管理需持续监测能量平衡行为，但传统自报方法存在严重低估和回忆偏差，且难与现代数字健康系统集成。本研究提出COBRA（慢性肥胖行为识别架构），一种基于腕戴多模态传感器的深度学习框架，用于客观行为监测。COBRA采用结合U-Net空间建模、多头自注意力机制与BiLSTM时序处理的混合D-Net架构，将日常活动分为四类：进食、身体活动、久坐行为与日常生活。在包含51名受试者、18种动作的WISDM-Smart数据集上验证，最优预处理策略融合频谱-时域特征提取，表现优异。D-Net整体准确率达96.86%，各类别F1分数分别为：身体活动98.55%、进食95.53%、久坐行为94.63%、日常生活98.68%，优于当前最优基线1.18%。模型表现出强泛化能力，人群差异小于3%，适用于个性化干预与持续生活方式监测的可扩展部署。
原文摘要 · Abstract (English)
Chronic obesity management requires continuous monitoring of energy balance behaviors, yet traditional self-reported methods suffer from significant underreporting and recall bias, and difficulty in integration with modern digital health systems. This study presents COBRA (Chronic Obesity Behavioral Recognition Architecture), a novel deep learning framework for objective behavioral monitoring using wrist-worn multimodal sensors. COBRA integrates a hybrid D-Net architecture combining U-Net spatial modeling, multi-head self-attention mechanisms, and BiLSTM temporal processing to classify daily activities into four obesity-relevant categories: Food Intake, Physical Activity, Sedentary Behavior, and Daily Living. Validated on the WISDM-Smart dataset with 51 subjects performing 18 activities, COBRA's optimal preprocessing strategy combines spectral-temporal feature extraction, achieving high performance across multiple architectures. D-Net demonstrates 96.86% overall accuracy with category-specific F1-scores of 98.55% (Physical Activity), 95.53% (Food Intake), 94.63% (Sedentary Behavior), and 98.68% (Daily Living), outperforming state-of-the-art baselines by 1.18% in accuracy. The framework shows robust generalizability with low demographic variance (<3%), enabling scalable deployment for personalized obesity interventions and continuous lifestyle monitoring.
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