Try these in your first Opus 5.5 session:
→ Hand over a whole task. Define "done" and when to check in.
→ Drop "think carefully". It always thinks first.
→ After a long run, check what it needs to go further.
Our playbook: https://t.co/h4Vz9BIl0z
Real-Time-Person-Elderly-Fall-Detection-System
Advanced Occlusion Resilience, Kinematic State Machine & Edge AI Architecture
Key Highlights
⚡ Instant-Trigger Fall Detection: Detects human falls the millisecond a rapid descent (vy>0.7 h/s) or posture collapse (Δθ/Δt>35∘/s) occurs, triggering immediate visual and dispatch alerts.
🛏️ Smart Resting / Sleeping False-Alarm Rejection: Biomechanically differentiates between an accidental fall and peaceful horizontal resting/sleeping on a bed (MONITORING (RESTING) in calm green, zero false sirens).
👁️ 4-Tier Occlusion Resilience:
i. Kinematic Bone Constraint Imputation: Mathematically reconstructs blocked knees and ankles using anatomical anthropometric ratios.
ii. Temporal Velocity Extrapolation: Carries momentum vectors through visual blockages.
iii. Furniture ROI Tracking: Monitors trajectories descending behind known occlusion barriers (coffee tables, beds, sofas).
iv. Proximity & Bone Length Clamping: Enforces strict skeletal bounds to eliminate spurious spiderweb lines.
🚀 100% Edge CPU Ready: Powered by a lightweight pose estimation engine (~6.5 MB). Runs at full speed on standard CPUs without requiring a dedicated GPU or custom model training.
🔄 Continuous Interactive Runner: One-click launcher with auto-looping menu, video auto-discovery, and drag-and-drop support.