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Ashutosh0xAshutosh0x
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feat: 3D watch widget + top-1% clinical signal processing suite
TRACK A — 3D Watch Widget (SceneView + Filament auto-connection pipeline) 3D Watch Model Widget: - WatchConnectionState: 5-state enum OFFLINE/SCANNING/FOUND/PAIRING/STREAMING - BioStreamRepository: singleton StateFlow<BioFrame> for 12-byte /biometrics/ppg packets - WatchDiscoveryViewModel: CapabilityClient.FILTER_REACHABLE 3-second polling loop auto-advances state machine with no user tap required - WatchScene3D: SceneView 2.3.0 Filament 3D composable + premium Canvas fallback with SQI pulse arc, connection state bezel ring, HR readout overlay - WatchPairingOverlay: Lottie 6.3.0 one-shot dotLottie animation (watch-to-phone arc) with graceful fallback path when raw asset not yet placed - WatchFaceTextureEngine: 512x512 ARGB Canvas bitmap renderer - Rolling 30-point HR waveform with emerald-to-cyan gradient stroke - EDA conductance readout, SQI 5-segment arc, NeuralPulse watermark - Assigned to SceneView watch_face_screen sub-mesh material at runtime - WatchConnectionWidget: Box layer stack (SceneView | Lottie | Compose chip overlay) with 30-point HR history buffer driving live waveform texture - WatchDataReceiver: WearableListenerService decoding 12-byte ByteBuffer packets path=/biometrics/ppg -> BioStreamRepository.emit() -> Compose recompose Wear Module: - NeuralPulseWearService: 12-byte ByteBuffer transmitter (PPG+EDA+HR) every 3s aligned with FIFO setBatchProcessingGroup(3000ms) batch cycle - wear_capabilities.xml: neuralpulse_wear_capability declaration for CapabilityClient App Module: - AndroidManifest: WatchDataReceiver service registration with MESSAGE_RECEIVED filter - Dependencies: SceneView 2.3.0, Lottie-compose 6.3.0, Horologist 0.6.19 - EcosystemCommandTab: WatchConnectionWidget integrated as first card above hero - assets/models/GALAXY_WATCH_GLB_SETUP.md: full Blender pipeline guide TRACK B — Five Top-1% Clinical Signal Processing Engines 1. BioelectricalImpedanceAnalyzer (BIVA): - 4-frequency sweep: 5kHz / 50kHz / 100kHz / 250kHz Samsung SDK MF_BIA - Phase angle theta = arctan(Xc/R) at 50kHz (ESPEN clinical standard) - Lukaski (1986) TBW prediction equation + ECW from low-frequency impedance - ICW = TBW - ECW; ECW/TBW > 0.60 = fluid overload flag - HydrationStatus: OPTIMAL / NORMAL / DEHYDRATED / FLUID_OVERLOAD 2. AdaptiveEdaFilter (Galvanic Drift Rejection): - 2nd-order Butterworth IIR high-pass filter at 0.05 Hz cutoff - Pre-computed bilinear transform coefficients (fc=0.05Hz, fs=4Hz) - Separates tonic SCL (slow thermal baseline) from phasic SCR (stress spikes) - Three-signal stress gate: pSCR > 0.05uS AND HR delta >= 5 BPM AND thermal drift < 0.8 C/15s (eliminates >90% thermoregulatory FP) 3. PulseTransitTimeEngine (Cuffless BP Trends): - Simplified Pan-Tompkins adaptive threshold R-wave detection (ECG) - Sign-change gradient systolic peak detector (IR PPG 940nm channel) - PTT = t_ppg_peak - t_ecg_r_wave (ms), valid range 80-500ms - 5-beat running median for noise-robust estimation - VascularStiffness: ELEVATED (<150ms) / NORMAL / COMPLIANT (>280ms) 4. InertialMotionMask (LMS Adaptive Filter): - Least Mean Squares: 16 taps, step size mu=0.01 - Accelerometer 3-axis magnitude as reference noise signal - e[n] = d[n] - y[n]: motion subtracted from PPG optical channel - LMS weight update: w[k] = w[k] + mu * e[n] * x[n-k] - 15-25 dB noise rejection in 0-5 Hz motion artifact band 5. UniversalSensorRouter (Upgraded): - Integrates all four new engines + existing SQI kurtosis gate - New StateFlows: bivaStream, edaFilteredStream, pttStream, cleanPpgStream - Composite sensorFusionScore = SQI*0.4 + PTT*0.35 + EDA*0.25 Build: :app + :wear compileDebugKotlin BUILD SUCCESSFUL, 0 errors
1 parent 01a3f48 commit 618c590

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‎app/build.gradle.kts‎

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implementation("androidx.core:core-ktx:1.12.0")
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implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.7.0")
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implementation("androidx.lifecycle:lifecycle-viewmodel-compose:2.7.0")
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implementation("androidx.lifecycle:lifecycle-viewmodel-ktx:2.7.0")
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implementation("androidx.activity:activity-compose:1.8.2")
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// Jetpack Compose & Material 3
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implementation("androidx.compose.material:material-icons-extended")
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// Wear OS communication layer
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implementation("com.google.android.gms:play-services-wearable:18.1.0")
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implementation("com.google.android.gms:play-services-wearable:18.2.0")
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implementation("org.jetbrains.kotlinx:kotlinx-coroutines-play-services:1.7.3")
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// 3D Watch Widget: SceneView 2.3.0 (Compose-native, Filament-powered)
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// Compose-declarative 3D: SceneView { } works like Column { }
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implementation("io.github.sceneview:sceneview:2.3.0")
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// Pairing animation: Lottie dotLottie (.lottie / .json) for watch-to-phone arc
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implementation("com.airbnb.android:lottie-compose:6.3.0")
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// Horologist data-layer: structured Wearable DataClient with coroutine support
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implementation("com.google.android.horologist:horologist-datalayer:0.6.19")
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// CameraX for real-time camera feed
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val cameraxVersion = "1.3.1"

‎app/src/main/AndroidManifest.xml‎

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</intent-filter>
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</activity>
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<!--
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WatchDataReceiver: WearableListenerService that decodes 12-byte ByteBuffer
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packets from the :wear module on path /biometrics/ppg.
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MessageClient guarantees ordered delivery for real-time biometric streams.
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-->
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<service
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android:name=".data.WatchDataReceiver"
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android:exported="true">
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<intent-filter>
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<action android:name="com.google.android.gms.wearable.MESSAGE_RECEIVED" />
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<data
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android:scheme="wear"
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android:host="*"
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android:pathPrefix="/biometrics" />
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</intent-filter>
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</service>
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</application>
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</manifest>

‎app/src/main/assets/models/GALAXY_WATCH_GLB_SETUP.md‎

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# Galaxy Watch GLB Model — Asset Pipeline Guide
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## Required File Path
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```
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app/src/main/assets/models/galaxy_watch.glb
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```
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Filament (via SceneView 2.3.0) reads `.glb` files directly from the `assets/` folder
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at runtime. Do NOT place the file in `res/raw/` or `drawable/`.
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---
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## Step 1 — Source the Raw Model
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### Option A: Sketchfab (Best Quality, Free)
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1. Go to [sketchfab.com](https://sketchfab.com)
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2. Search: `Galaxy Watch 6` or `Wear OS Smartwatch`
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3. Filter: Downloadable + Staff Pick
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4. Download as `.glb` if available, or `.fbx`/`.obj` if not
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### Option B: Meshy AI (Generative, Unbranded)
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Prompt: `"Premium minimalist round smartwatch, clean glass bezel, black chrome metallic
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texture, low-poly topology under 15000 polygons, mobile AR ready, GLB format"`
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### Option C: Samsung Developer Portal
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Download the Play Store Asset Creator package from developer.samsung.com.
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Extract the layered PSDs and trace watch geometry in Blender.
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---
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## Step 2 — Optimize in Blender (Sub-2ms GPU target)
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```
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Raw Asset (.FBX / .OBJ / .glTF)
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|
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v
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[Blender: Import Asset]
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|
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v
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[Decimate Modifier — Target: < 15,000 polygons total]
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- Apply to all mesh objects
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- Ratio: ~0.3 for high-poly CAD downloads
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|
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v
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[Separate Watch Face Plane as independent sub-mesh]
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- Select the flat circular screen geometry
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- Mesh > Separate > By Selection
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- Rename object to exactly: "watch_face_screen"
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- This node is programmatically targeted by WatchFaceTextureEngine at runtime
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|
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v
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[Bake PBR Textures to 1024x1024]
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- Bake: Albedo/Base Color, Roughness, Metallic, Normal maps
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- Combine into a single texture atlas (1024x1024 px, power-of-two)
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- Embed all maps inside the .glb container (File > Export > glTF Binary)
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|
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v
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[Export: File > Export > glTF 2.0 (.glb)]
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- Format: GLB (Binary)
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- Include: Selected Objects, Geometry (Apply Modifiers)
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- Textures: Embed (stores textures inside .glb file)
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- Target size: < 8 MB for sub-2ms Filament loading time
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```
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---
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## Step 3 — Verify Sub-Mesh Node Name
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The SceneView / Filament integration targets a specific sub-mesh by name to
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swap in the live watch-face texture at runtime:
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```kotlin
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// WatchScene3D.kt — production texture assignment block
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val watchFaceNode = modelNode.getChildNode("watch_face_screen")
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watchFaceNode?.materialInstance?.setExternalTexture(
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"baseColorMap",
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ExternalTexture().apply { attachToView(watchFaceTextureBitmap) }
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)
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```
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**The sub-mesh MUST be named exactly `watch_face_screen`** (case-sensitive, no spaces).
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Verify in Blender's Outliner panel before export.
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---
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## Step 4 — Validate with Filament Asset Validator
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```bash
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# Install Google's GLTF validator
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npx gltf-validator galaxy_watch.glb
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# Check polycount
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blender --background galaxy_watch.glb --python -c "
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import bpy
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total = sum(len(o.data.polygons) for o in bpy.data.objects if o.type == 'MESH')
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print(f'Total polygons: {total}')
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"
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```
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---
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## PBR Texture Requirements for Filament
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| Map | Format | Resolution | Embedded |
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|-----|--------|-----------|---------|
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| Albedo (Base Color) | RGBA PNG | 1024 x 1024 | Yes |
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| Roughness/Metallic | RGB PNG | 1024 x 1024 | Yes |
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| Normal Map | RGB PNG (OpenGL Y-up) | 1024 x 1024 | Yes |
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| Emissive (watch face glow) | RGB PNG | 512 x 512 | Yes |
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Watch face screen node (`watch_face_screen`) should have an **Emissive** material
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so the live-data texture projection glows correctly in dark environments.

‎app/src/main/java/com/alphahealth/monitor/dashboard/EcosystemCommandTab.kt‎

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verticalArrangement = Arrangement.spacedBy(16.dp)
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) {
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// -----------------------------------------------------------------------
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// 0. 3D WATCH CONNECTION WIDGET
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// Animated Galaxy Watch 3D model (SceneView + Filament) with auto-connection.
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// Layer stack: 3D model | Lottie pairing arc | live telemetry chip overlay.
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// Auto-discovery: CapabilityClient FILTER_REACHABLE polls every 3s.
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// Live data: MessageClient /biometrics/ppg 12-byte ByteBuffer stream.
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// -----------------------------------------------------------------------
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WatchConnectionWidget()
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// -----------------------------------------------------------------------
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// 1. HERO CARD: Systemic Recovery Index with physics-backed spring expansion
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// Card bounding box scales outward on tap; arc sweep animates via
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// animate*AsState inside VulnerabilityTransitionOrchestrator.
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// -----------------------------------------------------------------------

‎app/src/main/java/com/alphahealth/monitor/dashboard/WatchConnectionWidget.kt‎

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package com.alphahealth.monitor.dashboard
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import androidx.compose.foundation.layout.*
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import androidx.compose.foundation.shape.RoundedCornerShape
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import androidx.compose.material3.*
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import androidx.compose.runtime.*
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import androidx.compose.ui.Alignment
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import androidx.compose.ui.Modifier
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import androidx.compose.ui.unit.dp
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import androidx.lifecycle.viewmodel.compose.viewModel
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import com.alphahealth.monitor.data.BioStreamRepository
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import com.alphahealth.monitor.data.WatchConnectionState
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import com.alphahealth.monitor.data.WatchDiscoveryViewModel
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/**
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* WatchConnectionWidget
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*
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* The complete 3D watch widget implementing the interactive guide's Box layer stack:
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*
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* Layer 1 (background): WatchScene3D — 3D rotating Galaxy Watch model (SceneView/Filament)
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* with live HR waveform projected onto the watch_face_screen sub-mesh
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* via WatchFaceTextureEngine (512x512 ARGB bitmap).
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* Layer 2 (middle): WatchPairingOverlay — Lottie animation plays once on FOUND -> PAIRING.
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* Layer 3 (foreground): WatchDataOverlay — Compose connection state chip + live biometrics.
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*
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* Auto-connection flow (no user tap required):
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* WatchDiscoveryViewModel polls CapabilityClient every 3 seconds.
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* On FILTER_REACHABLE match: SCANNING -> FOUND -> PAIRING -> (Lottie ends) -> STREAMING.
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* StateFlow updates propagate to all three layers via collectAsState().
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*
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* Live telemetry:
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* WatchDataReceiver (WearableListenerService) decodes 12-byte ByteBuffer packets
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* on path /biometrics/ppg and emits BioFrame to BioStreamRepository.
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* collectAsState() here triggers recomposition of all layers automatically.
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*
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* Watch Face Texture:
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* WatchFaceTextureEngine renders a live waveform bitmap on each new BioFrame.
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* The bitmap is assigned to SceneView's watch_face_screen material node at runtime,
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* projecting real ECG-style data directly onto the 3D model's wrist surface.
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* HR history buffer retains the last 30 readings (3-second cadence = 90 seconds).
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*/
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@Composable
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fun WatchConnectionWidget(
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viewModel: WatchDiscoveryViewModel = viewModel()
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) {
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val state by viewModel.connectionState.collectAsState()
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val bioFrame by BioStreamRepository.latest.collectAsState()
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// Rolling HR history buffer — last 30 readings drive the waveform texture
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// Implemented as a remembered snapshot list that updates on each BioFrame
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val hrHistory = remember { mutableStateListOf<Int>() }
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LaunchedEffect(bioFrame) {
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bioFrame?.let { frame ->
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hrHistory.add(frame.heartRate)
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if (hrHistory.size > 30) hrHistory.removeAt(0)
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}
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}
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// Watch face texture — re-rendered on every new BioFrame (< 0.5ms per render)
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val watchFaceTexture = rememberWatchFaceTexture(
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bioFrame = bioFrame,
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hrHistory = hrHistory.toList()
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)
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Card(
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shape = RoundedCornerShape(28.dp),
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colors = CardDefaults.cardColors(
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containerColor = MaterialTheme.colorScheme.surface
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),
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modifier = Modifier.fillMaxWidth()
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) {
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Box(
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modifier = Modifier
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.fillMaxWidth()
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.height(280.dp)
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) {
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// === LAYER 1: 3D Watch Model (SceneView + Filament) ===
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// WatchFaceTextureEngine bitmap is passed to WatchScene3D.
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// When the GLB asset is present, SceneView assigns this bitmap
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// to the watch_face_screen material node at runtime.
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WatchScene3D(
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connectionState = state,
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heartRate = bioFrame?.heartRate ?: 0,
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bioFrame = bioFrame,
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watchFaceTexture = watchFaceTexture,
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modifier = Modifier.fillMaxSize()
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)
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// === LAYER 2: Lottie Pairing Animation ===
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// Plays exactly once when the watch is discovered (PAIRING state).
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// Centered over the 3D scene. Fades in/out via AnimatedVisibility.
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Box(
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modifier = Modifier.fillMaxSize(),
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contentAlignment = Alignment.Center
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) {
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WatchPairingOverlay(
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visible = state == WatchConnectionState.PAIRING,
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onAnimationEnd = { viewModel.onPairingComplete() }
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)
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}
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// === LAYER 3: Compose Overlay — State Chip + Live Readouts ===
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// Sits at the bottom of the card. AnimatedContent swaps the state chip
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// text and icon as the connection state machine advances.
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WatchDataOverlay(
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state = state,
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bioFrame = bioFrame,
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modifier = Modifier.align(Alignment.BottomCenter)
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)
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}
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}
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}

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