AI-Driven Panic Detection and Alert System Using Smartwatch and LLM Model

Technologically, most existing systems cannot detect distress autonomously through pattern recognition, further compounding the challenge of providing timely help. This research paper identifies the significant gap in the market for an automated, real-time alert system that provides a solution that does not require manual engagement. In response, an LLM-based Smartwatch-Based Panic Alert System (SBPAS) is proposed to integrate into smartwatches that continuously monitor heart rate, motion, and location biometric signals. By applying advanced machine learning models, the system detects abnormal patterns consistent with panic or distress and automatically triggers alerts to emergency contacts and local authorities. This eliminates the need for manual intervention, offering a faster, more reliable response in critical situations. The significance of this solution lies in its ability to reduce response times, improve the likelihood of timely assistance, eliminate the chance of not being notified about an emergency, and close the existing technological gap in safety infrastructure, particularly in regions where crimes against individuals are prevalent.