27 KiB
27 KiB
ADR-081: Gesture-Controlled Data Visualization
- Status: Proposed
- Date: 2026-04-07
- Deciders: ruv
- Relates to: ADR-079 (Camera Ground-Truth Training), ADR-029 (RuvSense Gesture Recognition), ADR-072 (WiFlow Architecture), ADR-076 (CNN Spectrogram Embeddings)
Context
RuView can now track 17 COCO keypoints at 92.9% PCK@20 (ADR-079) and detect gestures via DTW template matching (ADR-029). These capabilities exist independently — pose estimation produces skeleton coordinates, and the UI displays static charts. There is no system that connects hand/arm movements to interactive data exploration.
Gesture-controlled visualization would let users manipulate charts and graphs by waving their hands in front of the ESP32 sensing zone — no mouse, no touchscreen, no wearable. This is particularly valuable for:
- Lab/cleanroom — gloved hands can't use touchscreens
- Kitchen/workshop — dirty or wet hands
- Presentations — stand back and gesture at projected dashboards
- Accessibility — motor impairments that make mouse use difficult
- Digital signage — public displays without touch hardware
Why Camera + CSI Fusion
Camera alone can do gesture control (e.g., Leap Motion, MediaPipe Hands). CSI alone can detect coarse gestures (ADR-029). The fusion provides:
| Modality | Strengths | Weaknesses |
|---|---|---|
| Camera (MediaPipe Hands) | 21 hand landmarks, finger-level precision, 30fps | Requires line of sight, lighting dependent, privacy concern |
| CSI (ESP32) | Through-wall, works in dark, privacy-preserving, $9 | Coarse spatial resolution, no finger tracking |
| Fusion | Finger precision near camera + coarse tracking everywhere | Requires both sensors during training |
The fusion model trains on camera + CSI pairs (like ADR-079), then deploys in two modes:
- Camera-assisted — full precision when camera is available
- CSI-only — reduced but functional gesture control without camera
Decision
Build a gesture-to-visualization control system that maps hand/arm movements to chart interactions using fused camera + CSI input.
Gesture Vocabulary
Navigation Gestures (arm-level, CSI-detectable)
| Gesture | Motion | Chart Action | CSI Feasibility |
|---|---|---|---|
| Swipe left | Open hand sweeps left | Pan chart left / previous dataset | High — clear directional motion |
| Swipe right | Open hand sweeps right | Pan chart right / next dataset | High |
| Swipe up | Open hand sweeps up | Scroll up / zoom out | High |
| Swipe down | Open hand sweeps down | Scroll down / zoom in | High |
| Push forward | Palm pushes toward screen | Select / drill into data point | Medium — depth motion harder |
| Pull back | Hand pulls away from screen | Back / zoom out | Medium |
| Circular CW | Hand circles clockwise | Increase value / rotate view | Medium — temporal pattern |
| Circular CCW | Hand circles counter-clockwise | Decrease value / rotate back | Medium |
| Hold still | Hand stationary 2+ seconds | Hover / show tooltip | High — absence of motion |
| Both hands apart | Arms spread outward | Expand / zoom into selection | High — bilateral motion |
| Both hands together | Arms move inward | Collapse / zoom out | High |
Precision Gestures (finger-level, camera-required)
| Gesture | Motion | Chart Action | Sensor |
|---|---|---|---|
| Pinch zoom | Thumb + index spread/close | Continuous zoom | Camera only |
| Point | Index finger extended | Cursor position on chart | Camera only |
| Grab | Close fist | Grab and drag data point | Camera only |
| Thumb up | Thumbs up | Confirm / approve | Camera only |
| Thumb down | Thumbs down | Reject / undo | Camera only |
| Two-finger rotate | Two fingers twist | Rotate 3D visualization | Camera only |
| Finger slider | Index finger moves along axis | Adjust parameter value | Camera only |
Architecture
┌──────────────────────────────────────────────────────────────────┐
│ Input Layer │
│ │
│ ESP32 CSI (UDP 5005) ──→ CSI Gesture Detector (DTW + WiFlow) │
│ ↓ │
│ Webcam (MediaPipe Hands) ──→ Hand Landmark Tracker (21 joints) │
│ ↓ │
│ Gesture Fusion Engine │
│ ├── CSI coarse: swipe/circle/hold │
│ ├── Camera fine: pinch/point/grab │
│ └── Confidence weighting by modality │
└──────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────┐
│ Gesture Interpreter │
│ │
│ Raw gestures ──→ State Machine ──→ Chart Commands │
│ │
│ States: │
│ IDLE ──(motion detected)──→ TRACKING │
│ TRACKING ──(gesture matched)──→ ACTING │
│ ACTING ──(gesture complete)──→ COOLDOWN │
│ COOLDOWN ──(500ms)──→ IDLE │
│ │
│ Debounce: 200ms minimum gesture duration │
│ Cooldown: 500ms between consecutive gestures │
│ Confidence threshold: 0.7 for CSI, 0.9 for camera │
└──────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────┐
│ Visualization Controller │
│ │
│ Chart Commands ──→ WebSocket ──→ UI │
│ │
│ Commands: │
│ { type: "pan", dx: -0.1, dy: 0 } │
│ { type: "zoom", factor: 1.2, center: [0.5, 0.5] } │
│ { type: "select", x: 0.45, y: 0.62 } │
│ { type: "rotate", angle: 15 } │
│ { type: "slider", axis: "x", value: 0.73 } │
│ { type: "hover", x: 0.45, y: 0.62 } │
│ { type: "back" } │
│ { type: "confirm" } │
│ { type: "reject" } │
└──────────────────────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────────────────────┐
│ Visualization UI │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Line Chart │ │ Bar Chart │ │ 3D Scatter │ │
│ │ (time │ │ (category │ │ (spatial │ │
│ │ series) │ │ compare) │ │ data) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Heatmap │ │ Gauge │ │ Spectrogram │ │
│ │ (CSI grid) │ │ (vitals) │ │ (frequency) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ Visual feedback: gesture cursor overlay + action indicator │
│ Framework: D3.js / Observable Plot in existing UI │
└──────────────────────────────────────────────────────────────────┘
Gesture Detection Pipeline
CSI Gesture Detection (arm-level)
Extends the existing DTW gesture classifier (ADR-029) with WiFlow pose input:
CSI [35, 20] ──→ WiFlow lite ──→ 17 keypoints ──→ Extract arm features:
- Wrist velocity (dx/dt, dy/dt)
- Elbow angle (shoulder-elbow-wrist)
- Bilateral symmetry (left vs right)
- Motion energy (frame differencing)
↓
DTW template matching:
- 11 gesture templates
- Sliding window (1s)
- Top match + confidence
Camera Gesture Detection (finger-level)
Uses MediaPipe Hands (21 landmarks per hand, 30fps):
Webcam ──→ MediaPipe Hands ──→ 21 landmarks × 2 hands ──→ Extract:
- Finger states (extended/curled)
- Pinch distance (thumb-index)
- Grab state (all fingers curled)
- Point direction (index ray)
- Hand center velocity
↓
Rule-based classifier:
- Pinch: thumb-index < 0.05
- Point: only index extended
- Grab: all fingers curled
- Thumbs up/down: thumb angle
Fusion Strategy
CSI confidence ──┐
├──→ Weighted fusion ──→ Final gesture + confidence
Camera conf ──┘
Rules:
- If both agree: confidence = max(csi_conf, cam_conf) + 0.1 * min(csi_conf, cam_conf)
- If only CSI: use CSI gesture, confidence *= 0.8
- If only camera: use camera gesture, confidence *= 0.95
- If conflict: prefer camera for fine gestures, CSI for coarse gestures
- Minimum confidence for action: 0.6
Chart Interaction Mapping
Line Chart (Time Series)
| Gesture | Action | Parameters |
|---|---|---|
| Swipe left/right | Pan time axis | dx proportional to swipe speed |
| Pinch zoom | Zoom time axis | Continuous, centered on hand position |
| Both hands apart/together | Zoom (CSI-only alternative) | Binary zoom in/out |
| Point | Show tooltip at nearest data point | x from index finger position |
| Hold still | Sticky tooltip | Duration-based activation |
| Swipe up/down | Switch dataset / Y-axis scale | Discrete steps |
Bar Chart (Category Comparison)
| Gesture | Action | Parameters |
|---|---|---|
| Swipe left/right | Navigate categories | One category per swipe |
| Point | Highlight bar | Nearest bar to finger X position |
| Push forward | Select bar for drill-down | Depth gesture |
| Grab + drag | Reorder bars | Camera-only |
| Circular | Sort ascending/descending | Direction determines order |
3D Scatter Plot
| Gesture | Action | Parameters |
|---|---|---|
| Swipe left/right | Rotate Y axis | Angle proportional to speed |
| Swipe up/down | Rotate X axis | Angle proportional to speed |
| Two-finger rotate | Rotate Z axis | Camera-only |
| Pinch zoom | Zoom | Camera-only |
| Both hands apart | Zoom in (CSI alternative) | Binary |
| Point | Highlight nearest point | Ray-cast from finger direction |
Heatmap (CSI Grid)
| Gesture | Action | Parameters |
|---|---|---|
| Swipe | Pan view | dx, dy |
| Pinch | Zoom region | Center + scale |
| Hold | Show cell value | Position-based |
| Circular | Adjust color scale range | CW = expand, CCW = contract |
Gauge (Vital Signs)
| Gesture | Action | Parameters |
|---|---|---|
| Swipe left/right | Switch vital (HR → BR → SpO2) | Discrete |
| Circular CW | Set high alert threshold | Continuous |
| Circular CCW | Set low alert threshold | Continuous |
| Thumb up | Acknowledge alert | Binary |
Visual Feedback: AR Camera Overlay
The primary view is the live camera feed with AR overlays — the person is visible with charts, skeleton, and data rendered on top. This creates a "Minority Report" style interface where you see yourself manipulating data in real-time.
┌──────────────────────────────────────────────────────────────┐
│ │
│ ╔══════════════════════════════════════════════════════════╗ │
│ ║ ║ │
│ ║ [Live Camera Feed — person visible] ║ │
│ ║ ║ │
│ ║ ╭─────╮ ║ │
│ ║ │ │ ← skeleton overlay (17 keypoints) ║ │
│ ║ ╰──┬──╯ ║ │
│ ║ ╱ ╲ ║ │
│ ║ ╱ ╲ ┌──────────────────────┐ ║ │
│ ║ │ │ │ CSI Amplitude Chart │ ║ │
│ ║ │ 🖐→ │ │ ┌─╮ ╭─╮ ╭──╮ │ ║ │
│ ║ │ │ │ │ ╰─╯ ╰───╯ │ │ ║ │
│ ║ ╲ ╱ │ │ │ │ ║ │
│ ║ ╲ ╱ └──────────────────────┘ ║ │
│ ║ │ │ ↑ chart follows hand position ║ │
│ ║ ╱ ╲ ║ │
│ ║ ╱ ╲ ║ │
│ ║ ║ │
│ ╚══════════════════════════════════════════════════════════╝ │
│ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ LOWER THIRD │ │
│ │ ┌────┐ │ │
│ │ │ pi │ RuView Sensing HR: 72 BPM BR: 16 BPM │ │
│ │ │ │ v0.7.0 Presence: 1 Motion: 0.23 │ │
│ │ └────┘ │ │
│ │ [logo] [gesture: Swipe Right] [CSI ●] [CAM ●] [28fps]│ │
│ └──────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────┘
AR Overlay Layers (bottom to top)
| Layer | Content | Opacity | Update Rate |
|---|---|---|---|
| 0 | Live camera feed (full frame) | 100% | 30fps |
| 1 | Skeleton overlay (17 keypoints + bones) | 70% | 30fps |
| 2 | Gesture cursor (hand position + state) | 90% | 30fps |
| 3 | Floating chart (anchored to hand/body region) | 85% | 30fps |
| 4 | Data labels + tooltips | 95% | On gesture |
| 5 | Lower third (RuView branding + vitals + status) | 95% | 1fps |
Floating Chart Placement
Charts are anchored to the person's body and follow movement:
Placement rules:
- Default: chart floats to the right of the person's dominant hand
- If hand moves left: chart slides to left side
- Chart stays within frame bounds (never clips off-screen)
- Multiple charts: stack vertically with 10% gap
- Inactive charts: shrink to thumbnail and anchor near shoulder
Chart anchor point = wrist_position + offset(0.15, -0.1) // right and slightly above hand
Chart size: 30% of frame width × 20% of frame height
Lower Third Design
The lower third bar provides persistent status in broadcast-style framing:
┌──────────────────────────────────────────────────────────────┐
│ ┌──────┐ │
│ │ pi │ RuView Sensing v0.7.0 │
│ │ │ ────────────────────────────────────────────── │
│ │ logo │ HR: 72 BPM | BR: 16 BPM | Persons: 1 │
│ └──────┘ Motion: Low | Gesture: Swipe Right | 28fps │
│ [CSI ●] [CAM ●] [FUSE] PCK@20: 92.9% │
└──────────────────────────────────────────────────────────────┘
Design:
- Background: semi-transparent dark (#1a1a2e, 80% opacity)
- Logo: RuView "pi" icon (32x32px), left-aligned
- Text: white (#ffffff) primary, gray (#a0a0a0) secondary
- Accent: teal (#00d4aa) for active indicators
- Height: 15% of frame
- Font: system monospace for data, sans-serif for labels
- Divider: thin teal line separating logo from data
RuView Logo Placement
The "pi" logo appears in two contexts:
1. Lower third (persistent):
- Position: bottom-left corner, 12px padding
- Size: 32x32px
- Style: white outline on dark background
- Always visible during gesture mode
2. Watermark (optional):
- Position: top-right corner, 8px padding
- Size: 24x24px, 30% opacity
- Style: subtle, doesn't interfere with data
Skeleton Rendering Style
Keypoint rendering:
- Detected joints: teal circles (#00d4aa), radius 6px
- Low-confidence joints: gray circles (#666), radius 4px
- Active hand (gesturing): yellow highlight (#ffcc00), radius 8px, glow effect
Bone rendering:
- Normal bones: teal lines (#00d4aa), 2px stroke
- Active arm (gesturing): yellow lines (#ffcc00), 3px stroke, glow
- Torso: slightly thicker (3px) to anchor the skeleton visually
Style: dark-theme friendly, high contrast against camera feed
Cursor types:
- Open hand — teal ring around wrist, rays extending from fingers
- Pointing — teal ray from index finger toward chart
- Grabbing — yellow fist icon, chart border highlights
- Pinching — two teal dots (thumb + index) with distance line
- Ghost cursor — CSI-only mode: larger, more diffuse circle (no finger detail)
Data Flow Protocol
WebSocket messages from gesture engine to UI:
interface GestureEvent {
type: 'gesture';
gesture: 'swipe_left' | 'swipe_right' | 'swipe_up' | 'swipe_down'
| 'pinch_zoom' | 'point' | 'grab' | 'hold' | 'circle_cw'
| 'circle_ccw' | 'push' | 'pull' | 'spread' | 'contract'
| 'thumb_up' | 'thumb_down';
confidence: number; // 0-1
source: 'csi' | 'camera' | 'fusion';
position?: [number, number]; // Normalized [0,1] hand position
velocity?: [number, number]; // Hand velocity for proportional control
param?: number; // Gesture-specific parameter (pinch distance, rotation angle)
}
interface CursorEvent {
type: 'cursor';
x: number; // 0-1 normalized
y: number; // 0-1 normalized
state: 'tracking' | 'pointing' | 'grabbing' | 'pinching' | 'idle';
hands: number; // 0, 1, or 2
}
interface StatusEvent {
type: 'status';
csi_active: boolean;
camera_active: boolean;
mode: 'fusion' | 'csi_only' | 'camera_only';
fps: number;
gesture_count: number; // Total gestures detected this session
}
Training the CSI Gesture Model
Extends ADR-079's camera ground-truth pipeline:
# 1. Collect gesture training data (camera + CSI, 10 min)
# Perform each gesture 20+ times with natural variation
python scripts/collect-gesture-gt.py --duration 600 --gestures all --preview
# 2. Label gesture segments (auto-detected from camera)
node scripts/label-gestures.js \
--gt data/ground-truth/gestures-*.jsonl \
--csi data/recordings/csi-*.jsonl
# 3. Train gesture classifier
node scripts/train-gesture-model.js \
--data data/gestures/labeled-*.jsonl \
--scale lite
# 4. Deploy
# CSI-only mode: gestures detected from WiFlow keypoint motion
# Fusion mode: camera adds finger-level precision
Training data per gesture: ~20 examples × 11 gestures = 220 labeled samples. With augmentation (time warp, amplitude noise): ~1,000 effective samples.
Optimization: Temporal Gesture Encoding
Instead of classifying single frames, encode gesture trajectories:
Keypoint sequence [T=30 frames, 1 second]:
wrist_x[0..29], wrist_y[0..29],
elbow_angle[0..29],
hand_velocity[0..29]
↓
1D CNN (k=5, d=[1,2,4]) → 64-dim gesture embedding
↓
Nearest-neighbor to gesture templates (cosine distance)
↓
Top gesture + confidence
This is lighter than DTW for real-time use and can be trained end-to-end with the WiFlow backbone (shared TCN features).
File Structure
scripts/
collect-gesture-gt.py # Camera + CSI gesture data collection
label-gestures.js # Auto-label gesture segments from camera
train-gesture-model.js # Train CSI gesture classifier
gesture-server.js # WebSocket gesture detection server
ui/
components/
GestureOverlay.js # Cursor + feedback overlay
GestureChart.js # Gesture-controlled chart wrapper
GestureStatus.js # Sensor health bar
services/
gesture.service.js # WebSocket client for gesture events
Consequences
Positive
- Hands-free data exploration — manipulate charts without touching anything
- Works in dark/dirty/gloved conditions — CSI-only mode needs no camera
- Natural interaction — swipe, pinch, point are intuitive
- Builds on existing infrastructure — WiFlow + DTW + MediaPipe all exist
- Dual-mode deployment — degrade gracefully from fusion to CSI-only
- Low latency — WiFlow inference is 0.79ms, gesture detection adds ~5ms
Negative
- Learning curve — users must learn gesture vocabulary
- False positives — normal movement may trigger gestures (mitigated by state machine + cooldown)
- CSI-only precision — coarse gestures only without camera
- Single-user — multi-user gesture disambiguation is hard
Risks
| Risk | Probability | Impact | Mitigation |
|---|---|---|---|
| Gesture false positives from normal movement | Medium | High | State machine with IDLE→TRACKING threshold, 200ms debounce, 0.7 confidence gate |
| CSI gestures too coarse for chart control | Medium | Medium | Camera fallback for precision; CSI handles navigation-level gestures only |
| Latency > 100ms feels unresponsive | Low | High | WiFlow 0.79ms + gesture 5ms + WebSocket <10ms = ~16ms total |
| User fatigue ("gorilla arm") | Medium | Medium | Support seated gestures; small wrist movements, not full arm sweeps |
| MediaPipe Hands not detecting in low light | Medium | Low | CSI-only fallback; works in complete darkness |
Implementation Plan
| Phase | Task | Effort | Dependencies |
|---|---|---|---|
| P1 | gesture-server.js — WebSocket server with camera hand tracking |
3 hrs | MediaPipe Hands model |
| P2 | Camera gesture classifier (rule-based from hand landmarks) | 2 hrs | P1 |
| P3 | CSI gesture classifier (WiFlow keypoints → DTW templates) | 3 hrs | WiFlow model (ADR-079) |
| P4 | Fusion engine (confidence-weighted merge) | 2 hrs | P2 + P3 |
| P5 | GestureOverlay.js — cursor + feedback UI component |
2 hrs | P1 |
| P6 | GestureChart.js — gesture-controlled D3 chart wrapper |
4 hrs | P4 + P5 |
| P7 | Gesture training data collection + model training | 2 hrs | P3 |
| P8 | Integration with existing sensing UI | 2 hrs | P6 |
| Total | ~20 hrs |
References
- MediaPipe Hands — Google's 21-landmark hand tracking (30fps, CPU)
- ADR-029 — RuvSense DTW gesture recognition
- ADR-079 — Camera ground-truth training pipeline (92.9% PCK@20)
- Leap Motion — commercial gesture controller (comparison point)
- SolidJS/D3 gesture interaction patterns
- "GestureWiFi" (IEEE 2023) — WiFi gesture recognition survey