This is a submission for the Gemma 4 Challenge: Build with Gemma 4
What I Built
XiHan Snore Coach is an Android-based MedTech application designed to help users track, analyze, and alleviate snoring and sleep apnea symptoms.
In the medical health sector, privacy is paramount. Users are hesitant to upload highly sensitive physiological data—such as SpO2 (blood oxygen) levels, snoring audio metrics, and sleep disruption patterns—to cloud servers.
To solve this, I built an entirely offline, on-device AI Sleep Coach. It acts as a pocket medical analyst, allowing users to ask natural language questions about their sleep trends and receive actionable respiratory muscle training plans without a single byte of their personal data leaving their phone.
Demo
*https://youtube.com/shorts/ECAjo9r7Yew *
Code
https://github.com/yueliao11/sleepcoach.git
How I Used Gemma 4
To make this offline MedTech guardian a reality, I chose the Gemma 4 2B/4B Ultra-Mobile Model.
Here is why it was the absolute right tool for the job and how it powers the core experience:
- Perfect Edge-Deployment Architecture: Medical apps demand high reliability. The 2B/4B effective parameter sizes allowed me to run the model natively on an Android device. It delivers server-grade conversational UI fluidity directly from the user's pocket, even in Airplane mode during sleep.
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Native Tool Calling / Function Calling: Gemma 4 is doing real work at the heart of the app. Instead of just chatting, the AI utilizes Gemma 4's Function Calling capabilities. When a user asks "How was my sleep this week?", the Gemma 4 model intelligently triggers a local function (
get_local_sleep_history). This function queries the local Android Room/SQLite database for the last 7 days of SpO2 and Snore data, feeding it back into the model's context. - Advanced Reasoning: By feeding the extracted local physiological data back into the 128K context window, Gemma 4 correlates drops in SpO2 with snoring durations, ultimately generating highly personalized and safe oropharyngeal muscle training routines for the user.
Gemma 4 proved that we no longer have to compromise between advanced AI reasoning capabilities and strict patient data privacy.
This submission was published on behalf of the team by @your-dev-username.
Contributors:
- This submission was published on behalf of the team by @your-dev-username.
Contributors: