Personal health monitoring via IoT in LMICs is limited by affordability, low digital literacy, and limited health data comprehension. We present Guardian Angel, a low-cost, screenless wearable paired with a WhatsApp-based LLM agent that delivers plain-language, personalized insights. The LLM operates directly on raw, noisy sensor waveforms and is robust to the poor signal quality of low-cost hardware. On a benchmark dataset, a standard open-source algorithm produced valid outputs for only 70.29% of segments, whereas Guardian Angel achieved 100% availability (reported as coverage under field noise, distinct from accuracy), yielding a continuous and understandable physiological record. In a 96-hour study involving 20 participants (1,920 participant-hours), users demonstrated significant improvements in health data comprehension and mindfulness of vital signs. These results suggest a practical approach to enhancing health literacy and adoption in resource-constrained settings.

翻译：在低收入和中等收入国家，通过物联网进行个人健康监测受到可负担性、数字素养低以及健康数据理解能力有限的制约。我们提出了“守护天使”，一种低成本、无屏幕的可穿戴设备，搭配基于WhatsApp的LLM智能体，以提供通俗易懂的个性化健康洞察。该LLM直接处理原始、含噪声的传感器波形数据，并对低成本硬件产生的低信号质量具有鲁棒性。在一个基准数据集上，标准的开源算法仅对70.29%的数据段产生了有效输出，而“守护天使”实现了100%的可用性（报告为现场噪声下的覆盖率，区别于准确率），从而生成了连续且可理解的生理记录。在一项涉及20名参与者、为期96小时（总计1,920参与者小时）的研究中，用户表现出对健康数据的理解能力和对生命体征的关注度均有显著提升。这些结果表明，在资源受限的环境中，这是一种提升健康素养和采纳率的实用方法。