Advanced Human Life Detection Technology: Multi-Sensor Fusion Rescue System

The Advanced Human Life Detection Technology: Multi-Sensor Fusion Rescue System is a portable rescue tool. By fusing radar, audio, and vibration signals, combined with advanced signal processing and machine learning technologies, it addresses the limitations of traditional search and rescue methods (such as search dog fatigue, sound wave interference, and limited thermal imaging effectiveness). It improves the accuracy of locating survivors under rubble in disaster rescue and enhances environmental adaptability, providing reliable support for rescue work and striving for the golden rescue time.

After disasters such as earthquakes, building collapses, and mine accidents, the golden rescue time is only 72 hours. Quickly and accurately locating trapped people is the core challenge. Traditional search and rescue methods have limitations: search dogs are prone to fatigue, sound detection is disturbed by environmental noise, and thermal imaging has limited effectiveness in complex structures. The Advanced-Human-Life-Detection project proposes a portable multi-sensor fusion system to improve detection accuracy and environmental adaptability, providing more reliable technical support for rescue operations.

Radar Signal Detection

By transmitting microwave signals and receiving reflected waves, it detects chest movements caused by breathing and heartbeat. It can penetrate non-metallic obstacles (concrete, wood, etc.). Advanced filtering and signal enhancement technologies are required to extract weak physiological signals and provide distance information.

Audio Signal Analysis

High-sensitivity microphones capture sounds such as shouts, knocks, and breathing. It distinguishes human sounds from environmental noise through noise suppression, voice activity detection, directional analysis, and spectrum analysis.

Vibration Signal Monitoring

It detects vibrations caused by the activities of trapped people and identifies specific vibration patterns (different from environmental vibrations). It needs to analyze frequency characteristics, time-domain patterns, and spatial positioning.

Data Preprocessing

Unify sampling rates, filter noise, extract features (spectrum, time-domain statistics), and align data to standardize data from different sensors.

Machine Learning Models

• Support Vector Machine (SVM): Handles classification in high-dimensional feature spaces;
• Random Forest: Integrates decision trees to improve fusion accuracy;
• Deep Learning (CNN/RNN): Automatically learns complex signal patterns;
• Anomaly Detection: Identifies abnormal patterns to detect life signs.

Fusion Strategies

• Data-level: Fusion at the raw data level;
• Feature-level: Fusion after feature extraction (common method);
• Decision-level: Comprehensive after independent judgment by each sensor (strong fault tolerance).

System design needs to consider portability and ease of use: lightweight, rugged and durable, quick deployment, intuitive interface, and long battery life. Hardware components include: embedded computing platform (such as Raspberry Pi/Jetson), dedicated radar module, high-sensitivity microphone array, vibration sensor network, wireless communication module, and large-capacity battery/portable power supply.

Earthquake Rescue

Penetrate rubble to detect survivors in deep ruins and provide key location information;

Mine Accident Rescue

Quickly assess the location and status of trapped miners to guide rescue plans;

Building Collapse

Replace manual initial search to reduce the risk of rescue personnel;

Avalanche Search and Rescue

Penetrate snow layers to detect life signs and capture shouts or movement sounds.

Technical Challenges

• Environmental adaptability: Temperature, humidity, electromagnetic interference, etc., affect sensor performance;
• False positive control: Avoid misjudgment of non-life signals (mechanical vibration, animal activity, etc.);
• Real-time requirements: Quickly complete data collection, processing, and judgment.

Future Development

• More advanced sensors (terahertz radar, fiber optic sensors);
• Edge computing: On-device processing to reduce latency;
• 5G/6G communication: Remote support and multi-device collaboration;
• AR: Overlay detection results onto the field of view;
• Drone integration: Expand search range.

The Advanced-Human-Life-Detection project represents an important development direction of rescue technology. By fusing multi-sensing technology and machine learning, it provides a powerful tool for disaster rescue. In emergency moments, this technology improves rescue efficiency and strives for survival time for trapped people. Future technological progress will make rescue more efficient, save more lives, and also demonstrate the great potential of AI and sensor technology in the field of social public welfare.