LLM-Enhanced Wearables for Comprehensible Health Guidance in LMICs

TL;DR

This paper introduces Guardian Angel, a low-cost, screenless wearable device paired with an LLM via WhatsApp that provides accessible health insights in LMICs, improving data comprehension and health literacy.

Contribution

It presents a novel low-cost wearable and LLM system that operates on noisy sensor data and enhances health data understanding in resource-limited settings.

Findings

100% data coverage in noisy conditions

Significant improvement in user health data comprehension

Effective continuous health monitoring over 96 hours

Abstract

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.