AI-sCare+ Care Monitoring Image Analysis System
In care facilities facing severe staff shortages, there was an urgent need to reduce night monitoring burden and detect anomalies like falls and sudden health changes early. Privacy-conscious monitoring was a non-negotiable requirement.
Results Summary
Stable inspection quality: AI-based judgment dramatically reduces human error
Reduced workload: Automated recording and assessment cuts labor hours
Fast PoC-to-production: Evaluation kit enables rapid validation and smooth transition
Customer Challenge
In care facilities facing severe staff shortages, there was an urgent need to reduce night monitoring burden and detect anomalies like falls and sudden health changes early. Privacy-conscious monitoring was a non-negotiable requirement.
Implementation Overview
Results Summary
- Deployed across 8 facilities and 700 rooms, currently in live operation
- Night round burden dramatically reduced: AI-powered 24/7/365 monitoring
- Paperwork reduction through care record integration: automatic event logging cuts administrative overhead
Customer Challenge
- Night rounds demanded significant staffing, causing chronic fatigue among care workers
- Anomaly detection depended on individual staff experience, creating risk of missed or delayed discovery
- Manual care documentation consumed excessive time that should have been spent on resident care
- Balancing resident privacy protection with effective video-based monitoring was essential
Implementation Overview
- AI Cameras: Edge AI processing for privacy-conscious image analysis
- Biometric Sensors: Contactless heart rate, respiration, and body movement monitoring
- Toilet Occupancy Detection: Detecting prolonged stays and falls inside restrooms
- Temperature/Humidity Sensors: Integrated environmental monitoring for heatstroke and hypothermia risk
- AI Engine (RK3399): Real-time camera feed analysis
- Cloud Notifications: Instant alerts to staff devices upon anomaly detection
- iPad / PC Dashboard: Intuitive UI for easy operation by on-site staff
System Configuration
Results & Impact
- Night patrol rounds significantly reduced, measurably decreasing staff burden
- False-positive rate continuously improved, building on-site trust in the system
- Automatic care record generation dramatically cut manual documentation hours
- Local recording with video search enabled rapid overnight incident review
- Video data continuously feeds back into AI model training, steadily improving detection accuracy
Deployment Track Record
Currently deployed across 8 facilities and 700 rooms, supporting care monitoring, anomaly verification, and record integration. We accommodate customization for each facility's operational workflow and layout, with ongoing inquiries for additional deployments.
How CSUN Can Help
- Integrated camera, IoT, AI, and WebRTC: System-level optimization, not piecemeal solutions
- Phased deployment from PoC to full rollout: Start with a single-room evaluation
- Flexible design for real-world constraints: Adapts to existing facility layouts and lighting conditions
- Custom AI algorithm development: Custom models trained on your facility's data
- OEM / Customization: OEM supply available for partner companies
Related Images
Results & Impact
Deployed across 8 facilities and 700 rooms, currently in live operation. Night patrol burden significantly reduced; fall detection response time dramatically shortened vs. previous methods. Privacy-conscious design has been well received, with ongoing inquiries for additional deployments.
Why CSUN
Integrated camera, embedded, AI, and connectivity: System-level optimization, not piecemeal
One-stop from PoC to mass production: Prototype evaluation → design → production → long-term supply
Flexible design for real-world constraints: Adapts to existing lines, lighting conditions, and space limits
Trilingual support (JP/ZH/EN): Global development, sourcing, and support capabilities
Technical Details
| Deployment Field | Care, Healthcare & Monitoring |
|---|---|
| Monitoring Targets | Elderly residents (fall, wandering, sudden health changes), facility residents |
| Sensors | AI Cameras (RK3399+OV4689), biometric sensors, temperature/humidity, toilet occupancy |
| Video | WebRTC P2P live video, local recording (NAS/SD card) |
| AI Processing | Edge AI (RK3399), CNN pose estimation, fall detection, wandering detection |
| Notification | Instant alerts to iPad/PC (optional cloud notifications) |
| Client Devices | iPad, iPhone, Android, Windows PC |
| Recording | Local recording, video search & download, NAS/SD card support |
| Privacy | Edge processing + blurring, cloud-independent architecture |
| Result | Night round reduction, false-positive mitigation, automated care record integration, faster incident review |
| Deployment Track Record | Deployed across 8 facilities and 700 rooms |
Related Solutions & Products
From camera selection to AI model design, embedded implementation, and mass production — we provide end-to-end support.
FAQ
Can this work in a similar production environment?
In most cases, yes. Please share sample images and requirements from your site. We'll propose the optimal configuration based on similar cases.
Can I consult just for a PoC?
Absolutely. We welcome PoC-only engagements. Our evaluation kit enables rapid validation.
Can we validate without stopping our existing line?
Yes. Add-on cameras or non-invasive sensors allow validation while your existing line keeps running.
Can you help with camera selection?
Yes. We'll recommend the optimal camera module based on your target objects, lighting conditions, and inspection criteria.
Facing a similar challenge?
From camera selection to AI model design, embedded implementation, and mass production — we provide end-to-end support.