面向低资源护理场景的早产儿行为非接触式监测视觉嵌入式系统
Vision-Based Embedded System for Noncontact Monitoring of Preterm Infant Behavior in Low-Resource Care Settings
AI总结：
针对低资源地区早产儿行为监测依赖人工或侵入式设备的问题，提出基于量化MobileNet的树莓派嵌入式视觉系统，实现高精度实时监测，为低成本可扩展的NICU监测提供可行方案。
摘要：
早产仍是新生儿死亡的主要原因之一，在难以获得先进新生儿重症监护室（NICU）服务的低资源地区受影响尤为严重。持续监测婴儿的睡眠/清醒状态、哭闹发作等行为至关重要，但目前依赖人工观察或侵入式传感器，不仅容易出错、实用性差，还可能造成皮肤损伤。\n本文提出了一种新型无创自动化视觉框架以填补这一空白。我们开发了一套嵌入式监测系统，采用部署在Raspberry Pi（树莓派）上的量化MobileNet模型实现实时行为状态检测。在公开新生儿图像数据集上训练和评估后，该系统达到了当前最优的准确率（睡眠检测准确率91.8%，哭闹/正常状态分类准确率97.7%），同时保持了适合边缘部署的计算效率。通过对比基准测试，我们对模型大小、推理延迟与诊断准确率之间的权衡进行了关键分析。研究结果表明，尽管ResNet152、VGG19等更大的架构能带来小幅准确率提升，但其计算成本过高，无法满足实时边缘应用需求。\n该框架整合了三项核心创新：用于内存高效推理的模型量化（体积缩减68%）、针对Raspberry Pi优化的视觉流水线，以及用于临床警报的安全物联网通信。这项工作最终证明，MobileNet这类经过优化的轻量级模型，是构建可扩展、低成本且具备临床实用价值的NICU监测系统最可行的基础，为改善资源受限环境下的早产儿护理开辟了道路。
英文摘要：
Preterm birth remains a leading cause of neonatal mortality, disproportionately affecting low-resource settings with limited access to advanced neonatal intensive care units (NICUs).Continuous monitoring of infant behavior, such as sleep/awake states and crying episodes, is critical but relies on manual observation or invasive sensors, which are prone to error, impractical, and can cause skin damage. This paper presents a novel, noninvasive, and automated vision-based framework to address this gap. We introduce an embedded monitoring system that utilizes a quantized MobileNet model deployed on a Raspberry Pi for real-time behavioral state detection. When trained and evaluated on public neonatal image datasets, our system achieves state-of-the-art accuracy (91.8% for sleep detection and 97.7% for crying/normal classification) while maintaining computational efficiency suitable for edge deployment. Through comparative benchmarking, we provide a critical analysis of the trade-offs between model size, inference latency, and diagnostic accuracy. Our findings demonstrate that while larger architectures (e.g., ResNet152, VGG19) offer marginal gains in accuracy, their computational cost is prohibitive for real-time edge use. The proposed framework integrates three key innovations: model quantization for memory-efficient inference (68% reduction in size), Raspberry Pi-optimized vision pipelines, and secure IoT communication for clinical alerts. This work conclusively shows that lightweight, optimized models such as the MobileNet offer the most viable foundation for scalable, low-cost, and clinically actionable NICU monitoring systems, paving the way for improved preterm care in resource-constrained environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
