LILIN EDGETOF is a specialized front-end Edge AI analytics software license designed for health, elderly care, and assisted-living environments. Built for compatible Time-of-Flight 3D depth sensing cameras, it performs privacy-preserving fall detection, bed-leaving detection, and room vacancy monitoring directly at the camera level.
Model: LILIN EDGETOF; recognition type: ToF Recognition; plugin mode: mod011; product type: front-end Edge AI health and fall detection analytics; primary target behaviors: sudden patient falls, bed-leaving detection, and room vacancy; technology foundation: Time-of-Flight 3D spatial mapping and depth tracking; privacy method: point-cloud/depth tracking data; processing node: on-camera edge computation; integration: LILIN Open API for local networking relays.
• Uses ToF 3D depth sensing to detect falls and patient movement without standard video identification.
• Fall detection analyzes rapid height and point-cloud changes to trigger emergency notifications.
• Bed-leaving detection monitors a defined 3D zone around the mattress for unattended movement.
• Point-cloud depth tracking supports privacy-sensitive deployment in care rooms and private areas.
• On-camera edge computation eliminates the need for a centralized AI processing box.
EDGETOF relies on Time-of-Flight spatial depth mapping rather than conventional RGB footage. This allows the system to track body position, height change, and room occupancy while avoiding facial or identifiable image capture, making it suitable for hospital wards, elderly-care rooms, private bedrooms, and other sensitive environments.
Install EDGETOF on compatible ToF depth sensing hardware and define the monitored room, bed area, and fall-detection zones. Validate detection behavior with real placement, bed height, room layout, and patient movement patterns, and connect LILIN Open API relay actions to the required nurse-call, alarm, or local notification workflow.
| Technical Detail & Capability | |
| Model Code | EDGETOF (ToF Recognition) |
| Software Type | Front-end Edge AI Health & Fall Detection Analytics |
| Primary Target Behaviors | Sudden patient falls, bed-leaving detection, and room vacancy |
| Technology Foundation | Time-of-Flight (ToF) 3D spatial mapping and depth tracking |
| Privacy Protection | Point-cloud/depth tracking data (zero facial or identifiable image capture) |
| Processing Node | On-camera edge computation (no centralized AI processing box needed) |
| System Status Trigger | Native integration with LILIN Open API for instant local networking relays |