Identification of Victims Buried Under Avalanches

Abstract

An avalanche accident is a very hazardous incident, usually involving complete burial in compacted snow with reduced breathing capability and an acute reduction of available oxygen. The probability of survival decreases greatly with every minute of burial, and there is a marked drop beyond the first fifteen minutes. Conventional methods of avalanche rescue rely on the detection of the victim by radio beacons or reflectors. These methods do not provide any information on the physiological state of the buried victim. In this regard, rescue teams cannot access medical condition awareness as to which victim is in urgent need at the moment when multiple-victim avalanche situations arise. This paper proposes a wearable avalanche victim identification device that integrates a MAX30102 pulse oximeter sensor for detecting oxygen saturation and heart rate measurement, NEO-6M GPS module for acquiring geolocation, ESP32 microcontroller for signal processing, and SX1278 LoRa transceiver for long-range multi-hop wireless communication. The device automatically increases its transmission frequency when oxygen saturation falls below a threshold, indicating hypoxia. The experimental results report reliable physiological monitoring and long-range communication in mountainous terrain, with very effective support for rescue prioritization.

Introduction

Avalanches pose severe risks to skiers, hikers, and mountaineers, often burying victims under compacted snow. Once buried, chest movement is restricted, oxygen rapidly depletes, and survival probability drops sharply after 15–35 minutes. Existing avalanche rescue tools—transceivers, RECCO reflectors, probing, and search dogs—allow location detection but provide no physiological information. This lack of real-time medical data prevents effective triage in multi-casualty events, potentially leading to preventable deaths.

Objective

The study aims to design a wearable avalanche survival monitoring system that:

• Continuously measures oxygen saturation (SpO?) and heart rate.

• Tracks the victim’s location via GPS.

• Transmits data over a long-range LoRa mesh network.

• Automatically switches to emergency mode if hypoxia is detected.

• Operates autonomously without user input, is cold-resistant, power-efficient, lightweight, and compact.

Literature Review

• Hypoxia is the primary cause of death in buried but otherwise uninjured victims.

• MAX30102 pulse oximeter enables reliable SpO? and heart rate measurement even in low-perfusion, cold conditions.

• GPS and LoRa networks allow position tracking and long-range, low-power communication in mountainous terrain.

• Current systems lack integrated physiological monitoring, preventing informed rescue prioritization.

Existing Systems

• Avalanche beacons, RECCO reflectors, probing, and search dogs detect location but cannot assess medical condition.

• Absence of physiological data means rescuers cannot prioritize victims based on survivability.

Proposed System

• Hardware: MAX30102 pulse oximeter, NEO-6M GPS module, ESP32 microcontroller, SX1278 LoRa transceiver, rechargeable battery.

• Functionality: Continuously monitors SpO? and heart rate, calculates telemetry, and transmits via LoRa mesh.

• Emergency Response: Automatically detects hypoxic distress and increases transmission frequency to alert rescue teams.

• Provides autonomous, objective triage in real-time.

Methodology

• Photoplethysmographic waveforms are analyzed to calculate SpO?:

  SpO2=A−(B×R)SpO_2 = A - (B \times R)SpO2?=A−(B×R)

  where RRR is the absorption ratio of red and infrared light.

• Heart rate is computed from pulse intervals:

  HR=60/THR = 60 / THR=60/T

• ESP32 processes data, formats telemetry packets, and transmits via SX1278 LoRa.

• Emergency bursts are triggered if SpO? falls below a critical threshold.

Implementation

• System tested in field conditions showed:

  • Stable SpO? and heart rate measurements.

  • Reliable GPS retention under snow cover.

  • Multi-kilometer LoRa mesh transmission through obstructed terrain.

• Power efficiency ensured through deep-sleep mode between measurements.

• Demonstrated practicality for improving rescue efficiency and survivability in avalanche incidents.

Conclusion

This paper presents an innovative solution to the vital challenge of identifying and prioritizing avalanche victims based on physiological condition. The proposed wearable device for avalanche victim identification includes the MAX30102 pulse oximeter, NEO-6M GPS module, ESP32 microcontroller, and SX1278 LoRa transceiver, which together ensure reliable continuous health monitoring and geolocation tracking even in the harshest mountain environments. If signs of hypoxia are detected, the system autonomously increases transmission frequency, thus communicating medical urgency promptly to rescue teams, in turn allowing more effective data-driven decision-making in the process of multi-victim avalanche rescues. This system represents a significant improvement over traditional avalanche rescue tools, which largely do not possess health status awareness, by virtue of its integration of physiological monitoring with long-range, low-power communication in a rugged, wearable form factor. Field evaluations have demonstrated the feasibility of reliable SpO? and heart rate monitoring, stable GPS data retention, and multi-kilometer LoRa communication performance that validates the device\'s potential to improve survival outcomes in real-world avalanche scenarios. In the future, further power consumption optimization of the system, refining the triage algorithm in cases of multiple victims, and integration with existing avalanche rescue infrastructures could lead to further improvements. It has the potential to revolutionize avalanche rescue operations by providing rescue teams with objective real-time health data, thus increasing the chances of survival and reducing the possibility of preventable deaths in such high-risk environs.

References

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Copyright

Copyright © 2025 Ajin Kumar J, Mohamed Barakathulla L , Pravin M, Ayappan G, Dr. M Pachhaiammal @Priya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.