Inspiration
The idea for CIPHER came from a stark reality: 1 in 3 Nigerian adults has hypertension, yet most don’t know it. Existing consumer health apps are designed for Western lifestyles, diets, and healthcare access — they don’t reflect our culture, our foods, or our realities.
We wanted to create a solution that speaks to Nigerians, not just track data. Something that understands your story, nudges you before it’s too late, and empowers action, even when you feel fine.
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What We Learned • Behavioral psychology matters: Users act more on narrative and visual trends than raw numbers. • Passive sensing can detect subtle health signals: Accelerometer data revealed patterns that could indicate early motor changes. • AI + local context is powerful: Feeding culturally relevant datasets (e.g., egusi, stockfish, local cooking styles) into an LLM significantly improved actionable advice. • Hackathons are time pressure tests: Building a prototype in 24 hours forces focus on core differentiators.
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How We Built It • Frontend: React, Recharts for trend visualization, Web Speech API for voice companion, DeviceMotion API for passive tremor detection. • Backend: Node.js with Express, SQLite for lightweight storage, and a dedicated AI service layer. • AI Layer: Claude or OpenAI API for narrative generation and contextual risk assessment. Prompts include a curated Nigerian foods dataset for culturally specific advice. • Features: 1. Health Mirror — LLM-generated personal narrative. 2. Risk Dial — Animated trend indicator using BP, age, and lifestyle inputs. 3. BP Timeline — Historical BP chart for behavioral nudges. 4. Tremor Sentinel — Passive motion detection with contextual alerts. 5. CIPHER Voice — Ghost and Optimal conversational modes. 6. Shareable Health Report — One-click export for clinic visits.
Mathematically, the risk dial is computed with weighted inputs:
\text{Risk Score} = w_1 \cdot \text{BP Trend} + w_2 \cdot \text{Age Factor} + w_3 \cdot \text{Lifestyle Factor}
where w_1 + w_2 + w_3 = 1, and thresholds define green/yellow/red zones.
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Challenges Faced • Time constraints: 24 hours to design, implement, and integrate AI and passive monitoring. • Cultural dataset creation: We had to manually curate Nigerian foods and common lifestyle habits. • Passive tremor sensing: Ensuring accurate detection without false positives or alarming the user. • LLM behavior control: Preventing hallucinations or unsafe health advice while keeping advice actionable.
Despite these challenges, we built a fully functional prototype demonstrating the concept, ready for further clinical validation.
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