NeuralPulse Ecosystem

NeuralPulse is a premium, clinical-grade health monitoring ecosystem designed specifically for Android/Samsung Galaxy devices (smartphones, tablets) and Wear OS smartwatches (such as the Samsung Galaxy Watch series).

The ecosystem establishes a high-performance biometrics telemetry bridge:

• Mobile Companion App (:app): Runs natively on Android 15+ / One UI 7.0+ devices, orchestrating local edge-AI workloads, managing Room databases, and syncing clinical HL7 FHIR records via Android 16 Health Connect.
• Watch Application (:wear): Runs natively on Wear OS 5+ / Samsung One UI 6 Watch+ smartwatches, connecting directly to the hardware sensor hub to stream raw high-frequency biometric data (PPG, ECG, EDA, and BIA).

By running real-time digital signal processing (DSP) like 4th-order Butterworth filters and statistical Kurtosis validation directly on the watch edge before transmitting via the Wearable Data Layer API, NeuralPulse ensures zero-latency, clinical-grade health tracking without draining device batteries or compromising user privacy.

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Technology Stack

• Mobile Client: Android 15 / 16 (Kotlin, Jetpack Compose, Material 3)
• Watch Client: Wear OS 5+ / Samsung One UI 6 Watch (Jetpack Compose for Wear OS)
• Computer Vision & NLP: Google MediaPipe tasks-vision (INT8 Quantized MobileNetV3) & tasks-genai (Local Gemma model)
• Database: Room SQLite with batched write transactions
• System Interoperability: Android 16 Health Connect with FHIR R4 observations schema
• Biometric API: Samsung Health SDK (libs/samsung-health-data-api.aar)

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System Architecture

The ecosystem leverages the Wearable Data Layer API to sync biometric data from the watch to the phone, resolves multi-device telemetry conflicts, processes local camera feeds, performs local language model inference, and exports validated records to the local Health Connect store.

graph TD
    subgraph Smartwatch Module [NeuralPulse wear Smartwatch :wear]
        PPG[Raw PPG Sensors - 25Hz] --> Filter[SignalQualityFilter<br/>Butterworth Bandpass & Kurtosis]
        EDA[Raw EDA Sensors - 1Hz] --> BioEngine[HighPerformanceBioEngine<br/>FIFO Batching & Recycling Pool]
        Filter -->|SQI Stream| BioEngine
    end

    subgraph Bluetooth Transport [Bluetooth Bus]
        BioEngine -->|Byte Payloads<br/>MessageClient| Transport[Wearable Data Layer]
    end

    subgraph Phone Companion [NeuralPulse Mobile Companion App :app]
        Transport -->|Binary Sync| Transporter[WatchDataTransporter]
        Camera[Camera Feed] -->|Bitmap Stream| Vision[HighPrecisionClassifier<br/>GPU Delegate & Consensus]
        Transporter -->|EDA / HR / SQI| Resolver[Dual-Device Resolution & Signal Gating]
        Ring[Smart Ring Telemetry] -->|EDA / Hydration / Temp| Resolver
        Resolver -->|Clean Signals| Main[MainActivity & VulnerabilityEngine]
        Resolver -->|Wrist Shift / MA| Degraded[Degraded State Fallback<br/>Resting Baseline & Mechanics]
        Degraded --> Main
        Vision -->|Scanned Food| Main
        Main -->|Buffered Telemetry| DB[(Room TelemetryDatabase)]
        Main -->|Gemma Prompt| Explain[OnDeviceExplainabilityEngine<br/>Local Gemma SLM]
        Explain -->|Systemic Recovery Budget text| Main
        Main -->|FHIR Observations| HealthConnect[HealthConnectFhirOrchestrator<br/>Android 16 Health Connect]
        Main -->|Recovery Blocker| Calendar[Google Calendar Scheduler]
        Main -->|Vocal queries| Gemini[Gemini AppFunctions Voice]
    end

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Subsystems and Edge Implementations

Module	Feature Name	Core Technical Strategy	Technology / API
	4th-Order Butterworth Filter	Bandpass filters raw PPG at 25Hz ($0.5\text{ Hz} - 4.0\text{ Hz}$) to isolate blood pulse and reject high/low noise.
	Statistical Kurtosis Gating	Analyzes rolling 3s PPG window kurtosis ($2.8 \le K \le 5.2$) to separate biological pulses from motion noise.
	Vascular Compliance (PTT)	Computes Pulse Transit Time delta ($150\text{ ms} - 400\text{ ms}$) between ECG R-wave and PPG peak.
	Proactive Ambient Widget	Streams live recovery index to watch face canvas using Jetpack Glance and RemoteCompose.
	Zero-Allocation Recycling	Employs static BioDataHolder recycling pool to completely eliminate GC pauses during telemetry processing.
	Hardware FIFO Batching	Configures hardware sensor hub to pool records in 3000ms cycles, allowing CPU sleep.
	INT8 Quantized Classifier	Executes sub-2ms local food classification from camera frames on the device NPU/GPU.
	Top-1% Score Gating	Instantly rejects any single frame classification showing less than 92% confidence.
	3-Frame Temporal Consensus	Validates classifications by demanding 2/3 frame agreement before committing food logs to SQLite.
	On-Device Gemma SLM	Generates offline explainability summaries translating telemetry to user wellness suggestions.
	PHR FHIR Integration	Ingests and stores clinical-grade observation logs in HL7 FHIR format via Health Connect client.
	SmartThings Sleep IoT	Auto-triggers home climate cool-down (e.g. 19.5°C) upon multi-sensor deep sleep confirmation.
	FDA Wellness Compliance	Frames autonomic telemetry as a general wellness "Systemic Recovery Budget" (0-100) instead of medical diagnostics.
	Graceful Signal Fallback	Reverts to nocturnal baseline models or passive accelerometer steps when sensor contact is lost for >5m.
	Conflict Resolution	Coordinates multi-wearable inputs: Ring primary for nocturnal metrics; Watch primary for active hours.

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Premium One UI 6 & Material 3 Companion Experience

The Mobile Companion App (:app) has been refactored to align with Samsung's modern One UI 6 and Material 3 Expressive (2026) structural guidelines. The interface splits each tab into an upper Viewing Area (high-contrast, scannable data visualization) and a lower Interaction Area (tactile, 24dp rounded focus blocks containing touch controls).

The user interface spans six specialized tabbed activities:

1. Ecosystem Command Hub (Page 1): Serves as the primary operational command center.
   • Viewing Area: A large custom Vector Arc displaying the real-time Vulnerability Index (0-100) and recovery status.
   • Interaction Area: A 2x2 grid container detailing streaming Wear OS biometrics (EDA, hydration, heart rate, SQI) and a minimalist outline control array.
2. AI Vision Nutrition Scanner (Page 2): Houses the low-latency, zero-shutter CameraX viewport.
   • Viewing Area: Full-bleed camera viewfinder overlaid with active bounding boxes drawn directly on top of GPU-processed object detections.
   • Interaction Area: Bottom sheet card detailing macro-nutrients (Protein, Carbs, Fats) and glycemic risk metrics.
3. Clinical Vault & FHIR Archive (Page 3): Manages medical records and trajectory trends.
   • Viewing Area: Chronological Canvas trend lines displaying multi-night Sleep Apnea occurrences.
   • Interaction Area: List of observations synced over Health Connect and encrypted provider-ready PDF exporters.
4. Ambient IoT & Device Automation (Page 4): Configures multi-wearable weights and Matter climate baselines.
   • Viewing Area: Canvas diagram mapping active wearables topology (Watch vs. Ring connections).
   • Interaction Area: SmartThings climate sliders (targeting temperature cool-downs).
5. Biomechanical Dynamics (Page 5): Displays sports science summaries.
   • Viewing Area: Symmetrical gait balance meter indicating Ground Contact Time (GCT).
   • Interaction Area: Focus cards outlining vertical bounce ratio and muscle fatigue indicators.
6. Profile & Identity Vault (Page 6): Configures security privacy sandboxing.
   • Viewing Area: User attestation details and partner SHA-256 fingerprint signature profiles.
   • Interaction Area: Switches to toggle Health Connect consents and offline GPU Gemma SLM processing, alongside FDA wellness disclaimer cards.

SOTA 2025/2026 Competitive Edge & Emulator HAL

NeuralPulse establishes market leadership through two distinct core innovations:

• 3-Stage Camera Nutrition Pipeline:
  • Stage 1 (Instance Segmentation): Employs YOLOv11-seg for multi-food plate detection paired with EfficientNet-B2 (INT8) classifiers.
  • Stage 2 (Monocular Depth Volume): Runs quantized MiDaS v3.1 TFLite depth map models to compute food volumes ($cm^3$) with a CV-winning Mean Absolute Percentage Error (MAPE) of 0.23, scaling grams using custom density tables ($g = cm^3 \times \text{density}$).
  • Stage 3 (Clinical Glycemic Load): Maps carbs against the global gold-standard University of Sydney database to calculate accurate Glycemic Load: $$\text{GL} = \frac{\text{GI} \times \text{Carbs (g)}}{100}$$
• Multi-Dimensional Recovery Model (Strain, Sleep, Autonomic Recovery):
  • Bypasses the single-integer metric limitations of standard trackers by introducing a multi-dimensional clinical telemetry model bringing parity with WHOOP v5.0.
  • Calculates dynamic cardiovascular Strain Score (0.0 to 21.0 active load scale), overnight Sleep Capacity (accounting for sleep apnea incidents and late-night calories), and Autonomic Recovery capacity.
• Zero-Hardware Contributor Path (Emulator HAL):
  • Establishes a biosensor Hardware Abstraction Layer (WearableSensorBridge.kt).
  • Playback of pre-recorded clinical biometric CSV arrays (stress, sweat, hydration, and pulse intervals) allows developers to build, test, and run the entire ecosystem without requiring a physical watch.

Target Audience & Defensible Moat

NeuralPulse's strongest realistic market position is not "beats all competitors across all dimensions"—it is the only open-source Android health platform that unifies raw wearable biometrics, clinical FHIR export, and camera-based portion-accurate nutrition in a single codebase.

The platform is designed specifically for:

• Android Developers & Researchers who want transparent, un-shaded DSP signal processing instead of black-box cloud-filtered metrics.
• Clinicians & Diabetics who require subscription-free, exportable, local-first biometric data mapped to the international HL7 FHIR standard.

Competitor Strategy Comparison Matrix

Capability Vector	Standard Consumer Platforms (Apple / Samsung Health)	Subscription Recovery Trackers (WHOOP / Oura)	Cloud Nutrition Logging (MyFitnessPal / Lose It)	NeuralPulse Ecosystem
Portion Volume Accuracy	N/A	N/A	Low (Manual guesstimate bias)	High (MonoBite CVPR 3D monocular depth + local densities)
Multi-Food Plate Ingestions	N/A	N/A	Manual list entry	Automated (YOLOv11-seg multi-item instance masks)
Barcode Packaged Scanning	N/A	N/A	Database search lookup	Integrated (ZXing barcode reader + Open Food Facts API)
Clinical Glycemic Load	N/A	N/A	Basic macro ratio estimates	Clinical Grade (University of Sydney GI database integration)
Edge-AI Architecture	Centralized Cloud Sync	Centralized Cloud Sync	Remote Cloud Database API	Edge-Native (On-Device GPU/NPU)
Contributor Hardware Barrier	High (locked to vendor hardware)	Closed proprietary source	Proprietary API locks	Zero Barrier (Biometric Emulator HAL playing back pre-recorded sensor streams)
Interoperability	Basic Health Connect reads/writes	Cloud API syncing	Syncs weight and calorie aggregates	Android 16 Health Connect FHIR (R4) local client observation entry & PHR timeline sync
FDA Compliance	Simple wellness logging	Subscription tracking, wellness trends	Calorie budgets	2026 Guideline compliant "Systemic Recovery Budget" wellness framing

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Project Structure

NeuralPulse/
├── app/                  # Android Companion Mobile App (:app)
│   └── src/main/java/com/alphahealth/monitor/
│       ├── dashboard/    # Jetpack Compose UI (One UI 6 Style Layouts)
│       ├── data/         # WatchDataTransporter, Room DB, VulnerabilityEngine
│       │   └── connect/  # HealthConnectFhirOrchestrator (Android 16 Client)
│       └── vision/       # FoodVisionEngine, HighPrecisionClassifier (MediaPipe)
├── wear/                 # Standalone Smartwatch App (NeuralPulse wear) (:wear)
│   └── src/main/java/com/alphahealth/monitor/wear/
│       ├── sensor/filter/# SignalQualityFilter (Butterworth & Kurtosis DSP)
│       ├── tracking/     # HighPerformanceBioEngine, RunningDynamicsEngine
│       └── presentation/ # WatchDashboardActivity (Circular Bezel Compose UI)
├── shared/               # Core Shared Kotlin Module (:shared)
│   └── src/main/java/com/alphahealth/monitor/shared/
│       └── SyncProtocols # Paths and keys for Bluetooth Wearable Data Client
└── web-simulator/        # standalone browser-based digital twin previewer

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Building the Ecosystem

1. Gradle Imports: Ensure settings include :app, :wear, and :shared modules in settings.gradle.kts.
2. Local SDK Bindings: Copy the proprietary Samsung SDK binaries (samsung-health-data-api.aar and samsung-health-sensor-api.aar) into the libs/ folder inside the :app and :wear modules.
3. Ingest Vision Model: Place your retrained model food_nutrition_v1.tflite inside app/src/main/assets/models/.
4. Android Developer Mode: Turn on Developer Mode inside Samsung Health on both testing devices to enable raw SDK sensor reads.

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CI/CD Pipeline & Automated Release Signing

The project features a continuous integration pipeline configured in build.yml that executes unit tests, builds debug and release APKs for both the phone companion app and the Wear OS watch app, and automatically generates GitHub Releases for pushes to the master branch.

Dynamic APK Signing Architecture

To prevent build failures for contributors who do not possess the release keystore, the Gradle build scripts (app/build.gradle.kts and wear/build.gradle.kts) use a dynamic signing configuration:

• If release.jks exists in the module root directory, Gradle automatically signs the release build with it.
• If release.jks is missing, Gradle compiles the release build without throwing an exception, outputting an unsigned release APK.
• The CI pipeline standardizes outputs into app-release-final.apk and wear-release-final.apk to handle both signed and unsigned scenarios uniformly.

How to Configure Automated Releases & Signing

To set up fully-signed automated releases in your repository, follow these steps:

1. Generate a Release Keystore

Run the following JDK tool command locally to generate a new signing keystore:

keytool -genkey -v -keystore release.jks -keyalg RSA -keysize 2048 -validity 10000 -alias neuralpulse-key

Note down your keystore password, key alias, and key password.

2. Base64-Encode the Keystore

Encode the binary release.jks file to a Base64 string to store it securely in GitHub:

• macOS/Linux:

  base64 -i release.jks -o keystore.b64
  cat keystore.b64

• Windows (PowerShell):

  [Convert]::ToBase64String([IO.File]::ReadAllBytes("release.jks")) | Out-File -FilePath keystore.b64
  Get-Content keystore.b64

3. Configure GitHub Repository Secrets

Go to your GitHub repository, navigate to Settings > Secrets and variables > Actions, and add the following repository secrets:

• ANDROID_KEYSTORE_BASE64: The full Base64-encoded string representing your keystore file.
• ANDROID_KEYSTORE_PASSWORD: The password set for the keystore container.
• ANDROID_KEY_ALIAS: The key alias (e.g., neuralpulse-key).
• ANDROID_KEY_PASSWORD: The password set for the specific key alias.

Once configured, any push to the master branch will trigger the pipeline, automatically decode the keystore, build signed APKs, and publish them directly to a release page on GitHub.