基于大语言模型的增强型可穿戴设备用于低收入国家的可理解健康指导
LLM-Enhanced Wearables for Comprehensible Health Guidance in LMICs
AI总结：
Guardian Angel通过结合低成本可穿戴设备和基于WhatsApp的LLM代理，提供通俗易懂的健康指导，提升低收入国家用户对健康数据的理解和关注。
摘要：
在低收入国家（LMICs）中，通过物联网进行个人健康监测受到成本、数字素养低和健康数据理解能力有限的限制。我们提出了Guardian Angel，一种低成本、无屏幕的可穿戴设备，配以基于WhatsApp的LLM代理，提供通俗易懂的个性化见解。LLM直接在原始、嘈杂的传感器波形上运行，并对低成本硬件的差信号质量具有鲁棒性。在基准数据集上，标准开源算法仅在70.29%的段落中产生有效输出，而Guardian Angel实现了100%的可用性（报告为场噪声下的覆盖度，不同于准确性），产生了连续且易于理解的生理记录。在一项涉及20名参与者（1920名参与者小时）的96小时研究中，用户在健康数据理解和关注关键体征方面显示出显著改善。这些结果表明，这为在资源受限环境中提高健康素养和采用提供了一种实用方法。
英文摘要：
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.
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。
