Integrated Technologies For Proactive Bridge-Related Suicide Prevention

Abstract

Every year, over 1.3 lakh individuals commit suicide in India, posing a significant challenge to the country's healthcare system. Bridge-related suicides present unique difficulties, as it can be challenging to identify at-risk individuals in time for prevention. This study presents a comprehensive strategy for preventing bridge-related suicides by utilizing AI, IoT, and computer vision technologies. The method employs computer vision to detect individuals displaying suicidal tendencies on bridges and riverbanks. Additionally, the bridge is equipped with Internet of Things (IoT) sensors to monitor foot traffic and identify changes in behavior indicative of suicidal ideation. Data collected by computer vision and IoT sensors is analyzed by AI to determine individuals at risk of attempting suicide and the appropriate intervention timing. Preliminary research demonstrates promising results, with the computer vision system accurately identifying 90% of suicidal tendencies and IoT sensors detecting behavioral changes indicating higher suicide risk. The AI system identifies the most likely individuals to commit suicide within a group with an 80% accuracy rate.