LLM-Enhanced Wearables for Comprehensible Health Guidance in LMICs
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912890892582912 |
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| author | Ahsan, Mohammad Shaharyar Shaikh, Areeba Shahzad Zahid, Maham Irfan, Umer Mustafa, Maryam Bhatti, Naveed Anwar Alizai, Muhammad Hamad |
| author_facet | Ahsan, Mohammad Shaharyar Shaikh, Areeba Shahzad Zahid, Maham Irfan, Umer Mustafa, Maryam Bhatti, Naveed Anwar Alizai, Muhammad Hamad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08701 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM-Enhanced Wearables for Comprehensible Health Guidance in LMICs Ahsan, Mohammad Shaharyar Shaikh, Areeba Shahzad Zahid, Maham Irfan, Umer Mustafa, Maryam Bhatti, Naveed Anwar Alizai, Muhammad Hamad Human-Computer Interaction 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. |
| title | LLM-Enhanced Wearables for Comprehensible Health Guidance in LMICs |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.08701 |