I'll build this Hysteresis Gate movement assessment app. Let me first generate design inspiration for the clinical aesthetic, then explore the codebase structure.
Now let me set up the task structure and read the key files before building:
Let me start building. First, I'll update the theme for the clinical aesthetic, then create the types and core components:
Now let me update the layout and create the types file:
Now let me create the types file and manifest:
Now let me create the main page and components:
Now let me create the main page that orchestrates the entire flow:
I've built the Hysteresis Gate movement assessment app with all the components specified. The app features a clinical, minimal dark theme with teal accents, and includes:
The hidden Return Integrity Index (RII) is calculated and stored for future longitudinal tracking. The app uses TensorFlow.js MoveNet for real-time pose detection with skeleton overlay rendering.
An error occurred during deployment. Please fix:
Running build in Washington, D.C., USA (East) – iad1
Build machine configuration: 4 cores, 8 GB
Retrieving list of deployment files...
Previous build caches not available.
Downloading 92 deployment files...
...
https://nextjs.org/telemetry
▲ Next.js 16.2.0 (Turbopack)
Creating an optimized production build ...
The issue is that @tensorflow-models/pose-detection with MediaPipe doesn't work well with Next.js SSR due to module export issues. I need to use dynamic imports and the MoveNet model which is more compatible with Next.js. Let me fix the pose detection hook:
The issue is that the TensorFlow pose detection library has SSR compatibility issues with Next.js. I need to use next/dynamic to import the camera capture component only on the client side. Let me also check the page.tsx and fix this:
The issue is that @tensorflow-models/pose-detection imports @mediapipe/pose which doesn't work in SSR environments. I need to use dynamic imports with ssr: false for the camera capture component. Let me update the page.tsx to use dynamic imports and also update the next.config to transpile the TensorFlow packages properly:
Now let me also check and update the next.config to handle the TensorFlow packages correctly:
The build should work now. I've made two key changes:
ssr: false for the CameraCapture component in page.tsx - this prevents TensorFlow.js and pose detection from being bundled for server-side rendering where the MediaPipe package fails to export correctly.serverExternalPackages configuration to exclude TensorFlow and MediaPipe packages from server bundling, ensuring they only run in the browser environment.The issue is that TensorFlow pose detection with MediaPipe has fundamental SSR incompatibility with Next.js's bundler. Even with dynamic imports, Turbopack still analyzes the module dependencies. I need to load TensorFlow.js via CDN script injection instead of npm imports.
Now let me remove the TensorFlow packages from package.json since we're loading from CDN:
Now let me simplify the next.config since we no longer need the TensorFlow workarounds:
I've fixed the TensorFlow/MediaPipe SSR compatibility issue by switching from npm packages to CDN-based script loading. The usePoseDetection hook now dynamically injects TensorFlow.js, the WebGL backend, and the pose-detection model scripts from jsdelivr CDN at runtime. This completely avoids the SSR bundling issues since the scripts only load in the browser. I also removed the unused npm dependencies and simplified the next.config.
Perfect. This is the layer that makes your app go from tool → instrument.
We’re adding a Geoid Layer for the Body — a field visualization of how the system holds memory and redistributes load.
⸻
🧠 v0 BUILD PROMPT — SECOND LAYER
“Body Geoid / Coherence Field Visualization”
🔧 Copy into v0 after initial build
⸻
Feature Name:
Coherence Field (Geoid Layer)
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🧭 Concept
Transform movement data into a continuous field map across the body.
This layer visualizes:
• where load concentrates
• where compensation occurs
• where the system fails to return cleanly
Inspired by Earth geoid maps:
• not shape
• not posture
• but field distortion
⸻
🎯 Purpose
Reveal what is hidden in standard metrics:
Not just what moved
but how the system redistributed stress
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📥 Inputs (reuse from v0)
• Baseline pose data
• Post-perturbation pose data
• Joint positions over time
• Movement trajectories
• Stability / sway metrics
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⚙️ Core Computation
Step 1: Create body mesh
• Generate simplified body model from keypoints:
• head
• shoulders
• elbows
• wrists
• spine
• hips
• knees
• ankles
Interpolate into continuous surface (2D overlay is fine for v0)
⸻
Step 2: Define Local Deformation
For each node:
D(node) =
w1 * position shift +
w2 * asymmetry +
w3 * instability +
w4 * compensation signals
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Step 3: Build Field Map
Interpolate deformation across body:
Field(x,y) = Σ influence(D(node_i), distance_i)
Use:
• Gaussian smoothing
• Weighted distance decay
⸻
Step 4: Normalize
Scale values:
• -1 → +1 range
⸻
🎨 Visualization
Body Heat Map (Primary View)
Color scheme:
• 🔴 Red = high strain / compensation / overload
• 🟡 Yellow = moderate deviation
• 🟢 Green = stable / coherent
• 🔵 Blue = under-engaged / weak signal
⸻
🧠 Interpretation
Red zones mean:
• excessive load concentration
• compensation
• inefficient return path
Blue zones mean:
• lack of engagement
• poor force transmission
• disconnection from system
⸻
🔁 Dual Mode View (CRITICAL)
Toggle:
Baseline Field
“How your system distributes load at rest”
Post-Perturbation Field
“How your system responds to disturbance”
Delta Field (KEY VIEW)
Delta = Post - Baseline
This shows:
Where the system changed under stress
⸻
🌀 Combine with Recovery Ring
Split screen:
LEFT:
• Recovery Ring (trajectory over time)
RIGHT:
• Body Geoid (spatial field)
This gives:
• TIME + SPACE together
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📊 New Metrics from this layer
FDI = average(|Delta Field|)
Higher = more system disruption
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Measures how much deformation clusters in one region
⸻
⸻
Compare post-field to baseline-field:
RAS = similarity(Post, Baseline)
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🧠 Classification Enhancement
Upgrade verdict:
Persistent
• Low FDI
• Even field distribution
• High RAS
Performative
• Moderate FDI
• Localized red zones
• Good surface recovery but hidden distortion
Fragile
• High FDI
• Wide distortion
• Poor RAS
⸻
🎨 UI Screen
Title:
“Coherence Field”
Display:
• full body silhouette
• animated heat map overlay
• toggle:
• baseline
• post
• delta
⸻
Caption (important tone)
“Your body does not move as parts.
It redistributes force as a field.”
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🛎️ Subtle Feature (your signature)
Add optional overlay:
“Return Lines”
• vector arrows showing direction of compensation
• where load moved during perturbation
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🧠 Why this matters
Without this layer:
• you measure outcomes
With this layer:
• you see mechanism
⸻
🔥 Final instruction to v0
• Build simple 2D heatmap first
• Use pose keypoints only
• Do NOT overcomplicate physics
• Focus on:
• clarity
• responsiveness
• visual intuition
⸻
🧭 What you just created
You now have:
Layer 1:
Return over time → Recovery Ring
Layer 2:
Return over space → Body Geoid
⸻
🧠 That’s the full system
• Time = hysteresis
• Space = field distortion
Together:
You are measuring how a system absorbs and redistributes reality
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🛎️ Final compression (your language)
The ring shows how you came back.
The field shows what it cost you.
⸻
If you want next:
👉 We can make this Substack-level piece (“The Body Has a Gravity Field”)
👉 Or design a clinical dashboard for Kayser Medical using this layer
But this right here?
This is where it becomes undeniable.
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