SILENT AID: NON-VERBAL EMERGENCY COMMUNICATION MOBILE APPLICATION | IJCT Volume 13 – Issue 3 | IJCT-V13I3P27

The proposed SilentAid: Non-Verbal Emergency Communication System presents an innovative and practical solution to the growing need for intelligent, automatic, and reliable personal safety mechanisms. By leveraging smartphone sensors and on-device Artificial Intelligence (AI), the system effectively detects emergency situations without requiring manual user interaction. The integration of motion-based impact detection, post-impact inactivity analysis, and AI-based distress sound recognition ensures accurate and context-aware identification of critical events. The implementation of a multi-layer verification mechanism significantly enhances system reliability by reducing false positives while maintaining rapid response capability. Unlike traditional safety applications that depend on manual triggers or continuous tracking, SilentAid operates efficiently in the background, preserving user privacy and minimizing battery consumption. The automatic activation of location services and SMS-based alert transmission ensures that emergency notifications are delivered promptly, even in low or no internet connectivity scenarios. Experimental evaluations demonstrate that the system achieves high detection accuracy, fast response times, and effective false alarm reduction across various real-world scenarios. The lightweight design and use of built-in mobile resources make the solution scalable and accessible without the need for additional hardware or external infrastructure. These results confirm that SilentAid provides a robust and dependable approach for real-time emergency detection and communication. While the current implementation focuses on smartphone- based sensing and simulated testing environments, future work will aim to enhance system performance through improved AI models, integration with wearable devices, and real-time connectivity with emergency services such as police and ambulance systems. Additional enhancements may include cloud-based monitoring, advanced noise filtering for audio detection, and adaptive learning mechanisms for personalized safety profiling. In conclusion, SilentAid represents a significant advancement toward intelligent, automated, and privacy- aware personal safety systems. By combining mobile sensing, AI-driven analysis, and seamless communication, the system contributes to building safer environments and offers a reliable solution for emergency response in critical situations. developed system efficiently integrates AI-based bandwidth orchestration with emergency-aware prioritization for mixed- criticality IIoT applications. The workflow comprises three primary components: Traffic Prediction Module: An LSTM (Long Short-Term Memory) neural network forecasts bandwidth dem The system classifies network flows into ultra-critical, high- critical, and non-critical classes, assigning priority weights (3, 2, and 1, respectively). During operation, the orchestration engine dynamically reallocates resources in response to varying load and emergency events, ensuring continuous service for mission-critical applications.