Lifescan: Real-time Survivor Detection Using Non-contact Vital Sign Monitoring And Deep Learning

Abstract

Lifescan Offers A Non-invasive, Efficient, And Reliable Approach To Locating Survivors, Significantly Reducing Rescue Time And Improving The Chances Of Saving Lives. This Technology Has Potential Applications In Disaster Management, Military Operations, And Remote Healthcare MonitoringLifescan: Real-Time Survivor Detection Using Non-Contact Vital Sign Monitoring And Deep Learning Is An Advanced System Designed To Enhance Search And Rescue Operations In Disaster Scenarios. The Proposed System Utilizes Non-contact Sensing Technologies Such As Optical Cameras, Thermal Imaging, And Radar Sensors To Detect Human Vital Signs, Including Heart Rate And Respiration, Without Requiring Physical Contact. These Physiological Signals Are Often Difficult To Capture In Challenging Environments Such As Collapsed Structures Or Low-visibility Conditions.To Address This, The System Integrates Deep Learning Algorithms For Accurate Detection And Classificationof Human Presence Based On Extracted Vital Signals. The Collected Data Is Processed Through Signal Filtering And Feature Extraction Techniques, And Then Analyzed Using Trained Neural Network Models To Determine The Likelihood Of Survival. By Combining Sensor Data With Intelligent Analysis, The System Can Identify Survivors In Real Time, Even If They Are Unconscious Or Immobile.Lifescan Offers A Non-invasive, Efficient, And Reliable Approach To Locating Survivors, Significantly Reducing Rescue Time And Improving The Chances Of Saving Lives. This Technology Has Potential Applications In Disaster Management, Military Operations, And Remote Healthcare Monitoringthe Proposed System Emphasizes Robustness And Adaptability In Dynamic And Noisy Environments Commonly Encountered During Disaster Situations. Advanced Preprocessing Techniques Are Employed To Minimize Interference Caused By Dust, Debris, And Environmental Disturbances, Ensuring Reliable Signal Acquisition. The Deep Learning Models Are Trained On Diverse Datasets To Improve Generalization And Accuracy Across Different Scenarios, Including Varying Lighting Conditions And Partial Occlusions. The Conductected Source Can Developed.

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