基于CNN-BiLSTM模型的可穿戴传感器久坐行为分类

中文摘要 AI 辅助

准确检测久坐行为对研究长期坐姿相关的健康风险十分重要，但利用可穿戴传感器（尤其是腕部传感器）进行基于姿势的分类仍具挑战性。本研究探究基于髋部佩戴加速度计数据训练的深度学习模型能否迁移至腕部加速度计数据，以实现坐/非坐分类。我们使用最初为髋部加速度计开发的CNN-BiLSTM模型CHAP，评估其在腕部数据上的零样本性能，以及利用不同数量的标记腕部数据进行微调后的适配效果。实验在iWatch数据集上开展，该数据集的姿势真值标签来自可穿戴相机。经髋部数据训练的模型在髋部数据上表现强劲，无需重新训练，但因传感器位置偏移，在腕部数据上的准确率下降。微调CHAP模型相比从头训练的Transformer模型具有持续优势。这些发现表明，基于髋部的预训练为腕部部署提供了有用的起点，同时凸显了针对腕部进行适配以应对更高信号变异性的必要性。

英文摘要

Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains challenging with wearable sensors, especially at the wrist. We study whether a deep learning model trained on hip-worn accelerometer data can transfer to wrist-worn accelerometer data for sitting versus non-sitting classification. We use CHAP, a CNN-BiLSTM model originally developed for hip accelerometers, and evaluate its zero-shot performance on wrist data as well as its adaptation through finetuning with varying amounts of labeled wrist data. Experiments are conducted on the iWatch dataset with ground-truth posture labels derived from wearable cameras. The hip-trained model performs strongly on hip data without retraining, but accuracy drops on wrist data due to sensor placement shift. Finetuning CHAP provides consistent advantages over transformer models trained from scratch. These findings suggest that hip-based pretraining provides a useful starting point for wrist deployment, while highlighting the need for wrist-specific adaptation to handle higher signal variability.

发表机构

• Halıcıoğlu Data Science Institute, University of California, San Diego（加州大学圣迭戈分校哈利乔格鲁数据科学学院）
• Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego（加州大学圣迭戈分校赫伯特韦特海姆公共卫生与人类长寿科学学院）
• Department of Computer Science and Engineering, University of California, San Diego（加州大学圣迭戈分校计算机科学与工程系）
• Center for Children’s Healthy Lifestyles & Nutrition, Children’s Mercy Kansas City, University of Missouri-Kansas City（密苏里大学堪萨斯城分校儿童慈善医疗堪萨斯城分院儿童健康生活方式与营养中心）
• Kaiser Permanente Washington Health Research Institute（凯撒永久华盛顿健康研究所）
• Department of Kinesiology and Nutrition, University of Illinois Chicago（芝加哥伊利诺伊大学运动机能学与营养系）

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