| I&T Solution |
Integrated AI Confined Space Safety Monitoring System with Multi-Frame Fall Detection
(REF: S-1955) |
| Trial Project |
|
| Solution Feature |
- By considering the dynamic process of a fall through Multi-Frame Camera Analytics rather than a static image, multi-frame methods achieve better accuracy and reliability.
- Use of temporal Convolutional networks or LSTM models on skeleton sequences to model motion dynamics distinguishing collapse from controlled lowering.
- Combination of pose persistence and motion drop reduces false positives caused by intentional lying down or working in a prone position.
- Hybrid systems combining vision with wearable sensors to improve confirmation.
|
| Trial Application and Expected Outcome |
- Reducing false alarms and ensuring reliable alerts.
- AI automatically detects worker fainting, falls, and abnormal inactivity through continuous video analysis.
- Real-time risk assessment by integrating physiological and environmental sensor data.
- Automated early warning and alarm system to enable timely intervention by safety personnel.
- Enhanced situational awareness and risk prevention capability, improved worker safety, reduced accident rates, and optimized emergency response processes.
|
| Additional Solution Information |
Integrated AI Confined Space Safety Monitoring System with Multi-Frame Fall Detection.pptx
|
| Info on I&T Solution Provider |
| Solution Provider | : | ATAL Engineering Limited | | Address | : | ATAL Tower, 45-51 Kwok Shui Road, Kwai Chung, New Territories, Hong Kong | | Contact Person | : | Cheung, Eric King Yip |
| Position | : | Senior Technical Manager | | Tel | : | +852 98644274 | | Email | : |
ericcheung@atal.com | | Webpage | : | www.atal.com |
|
