HemOptima: Smart Internal Bleeding Detection System for Accidental Injuries

Abstract

The increasing rate of road accidents and trauma cases highlights the need for rapid emergency diagnosis, especially for internal bleeding, which often remains undetected during transportation. HemOptima is an IoT- and Machine Learning–powered smart health monitoring system designed to assist ambulance teams by providing real-time internal bleeding detection. The system continuously measures vital parameters, including heart rate, systolic and diastolic blood pressure, SpO?, and temperature using embedded sensors, and processes them through a Raspberry Pi simulation module. A Random Forest Classifier, trained on structured physiological datasets, analyses these readings and predicts internal bleeding with 92% accuracy, enabling faster and more reliable assessment. When a critical condition is detected, HemOptima automatically transmits vital data and prediction status to the nearest hospital while logging all measurements in an SQL database for future reference and clinical evaluation. Unlike traditional emergency setups that rely solely on manual diagnosis, HemOptima integrates IoT monitoring, ML-driven prediction, and automated communication to enhance pre-hospital care, reduce diagnostic delays, and improve patient survival outcomes

Introduction

1. Need for Intelligent Emergency Medical Systems

• Road accidents and trauma are major causes of preventable deaths, with internal bleeding being a time-critical condition.

• Traditional emergency care often fails to detect internal hemorrhage promptly due to lack of visible symptoms.

• Advances in IoT, Machine Learning (ML), and smart sensors enable real-time monitoring, automated diagnosis, and faster hospital notifications, potentially improving survival during the “golden hour.”

2. Limitations of Traditional Emergency Response

• Ambulances rely on manual assessment and basic monitoring tools, insufficient for detecting internal bleeding.

• Emergency alerts are often slow, and false alarms can overwhelm responders.

• Existing hospital-based ML or imaging systems cannot be deployed in ambulances for real-time monitoring.

• Delays in diagnosis prevent timely hemostatic interventions, reducing patient survival rates.

3. Existing Solutions

• Image-Based Hemorrhage Detection: ML/deep learning models detect bleeding from CT or arthroscopic images but are limited to hospitals.

• IoT Accident Detection Systems: Identify accidents and alert responders but cannot assess patient vitals or internal bleeding.

• Clinical Treatments: Focus on hospital-based interventions, not early pre-hospital detection.

• Gap: No system provides continuous, real-time internal bleeding detection during ambulance transport.

4. Proposed System – HemOptima

HemOptima integrates IoT sensors, ML-based prediction, automated hospital alerts, and data storage to provide real-time internal bleeding detection.
Key Components:

1. IoT-Based Vital Monitoring:

   • Sensors like MAX30102 (heart rate, SpO?), DS18B20 (temperature), and BP module collect real-time vitals.

   • Detects early signs of bleeding: hypotension, low oxygen saturation, altered heart rate.

2. Machine Learning Prediction:

   • Random Forest classifier trained on vitals predicts bleeding with 92% accuracy.

   • Provides binary output (Bleeding/Not Bleeding) and confidence scores.

3. Automated Hospital Alerts:

   • Sends patient vitals and prediction results to the nearest hospital in real time.

   • Allows hospitals to prepare surgical teams and resources before arrival.

4. SQL-Based Data Logging:

   • Stores vitals, predictions, timestamps, and alerts for medical review and system evaluation.

5. Methodology & Workflow

1. Continuous Data Capture: IoT sensors stream vitals to Raspberry Pi.

2. Real-Time ML Processing: Data preprocessed and analyzed by Random Forest classifier.

3. Prediction & Alerting: Immediate notification sent to hospitals upon detection of bleeding.

4. Data Storage: All information logged in SQL database for traceability, analysis, and improvement.

6. Results & Performance

• HemOptima successfully integrates IoT, ML, and alerting for pre-hospital internal bleeding detection.

• Random Forest classifier achieved 92% accuracy in real-time prediction.

• Sensor readings remained stable, and hospital alerts were transmitted reliably.

• SQL logging enables structured medical record-keeping.

• Demonstrated improvement over traditional ambulance systems that lack predictive and continuous monitoring capabilities.

7. Advantages

• Real-time detection of internal bleeding during transport.

• Automated hospital notifications reduce response time.

• Multi-sensor integration captures subtle physiological changes.

• Structured data logging supports medical review and model refinement.

• Enhances pre-hospital emergency preparedness and potentially improves patient survival.

Conclusion

Road accidents and trauma injuries continue to be major contributors to preventable deaths worldwide, with internal bleeding identified as one of the most dangerous and time-critical conditions [1]. Unlike external injuries, internalhemorrhage is difficult to detect without medical equipment, resulting in delayed diagnosis and reduced survival chances during the “golden hour.” Recent research emphasizes the importance of rapid emergency response, automated detection, and intelligent monitoring systems to reduce fatalities [3]. Advancements in IoT, Machine Learning (ML), and smart sensing technologies have enabled real-time monitoring and automated diagnosis in fields such as trauma care and medical imaging. ML-based detection systems have already shown strong performance in identifying hemorrhage patterns in clinical settings, including brain hemorrhage detection [1] and arthroscopic bleeding recognition [5]. Similarly, IoT-enabled accident detection platforms highlight the life-saving impact of real-time alerts and automated emergency reporting [4

References

[1] Z. Chen, X. Xu, and Y. Li, “A Smart Machine Learning Model for the Detection of Brain Hemorrhage Diagnosis Based on Internet of Medical Imaging,” Complexity, vol. 2020, pp. 1–10, 2020. [2] Y. Hong, J. Zhao, Y. Liu, and J. Kim, “Engineering a Two-Component Hemostat for the Treatment of Internal Bleeding,” Advanced Healthcare Materials, vol. 12, no. 8, pp. 1–11, 2023. [3] M. R. Hd, “SENS: Smart Emergency Notification System,” International Journal of Engineering and Technology Research, vol. 3, no. 2, pp. 1–7, 2018. [4] S. A. Abed, M. Abdullah, and H. Mahmood, “IoT-Based Accident Detection and Smart Emergency Response System,” Research Journal of Science and Engineering Systems, vol. 3, no. 1, pp. 91–96, 2018. [5] W. Liu, S. Zheng, and Y. Zhang, “Research on Arthroscopic Images Bleeding Detection Algorithm Based on ViT-ResNet50 Integrated Model and Transfer Learning,” in Proceedings of the International Conference on Medical Imaging and Intelligent Processing, pp. 1–8, 2023.

Copyright

Copyright © 2025 Shama Anjum D, Deepak R S, Vijayashanthi T. 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.