Real-Time Hand and Face Gesture Recognition for Emergency Response

Abstract

This paper presents an innovative system for emergency hand recognition, integrated with facial recognition technology, to provide rapid access to an individual’s medical historyduringcriticalsituations.Theproposedsolutionleverages advanced biometric analysis to identify individuals via hand and facial features, ensuring quick and reliable recognition. Upon identification, the system retrieves and displays the person’s medical records, aiding emergency responders in delivering precise and timely care. This dual-modality approach enhances accuracy, reduces response time, and demonstrates significant potential in improving emergency healthcare outcomes. The system’s design,implementation,and real-worldapplications are explored, emphasizing its utility in healthcare emergencies.

Introduction

The paper proposes a novel emergency response system combining hand gesture recognition and facial recognition to enable rapid, accurate, and personalized communication during emergencies. Using computer vision and machine learning tools like MediaPipe for hand landmarks, Random Forest classifiers for gesture classification, and face recognition libraries, the system detects predefined emergency gestures and identifies individuals to retrieve their medical histories in real time. Alerts are sent via SMS and email, and voice feedback is provided to assist responders effectively.

The system architecture includes input capture, preprocessing, classification, integration, communication, and output layers, making it practical for real-world deployment. This dual-modality approach addresses communication challenges faced by individuals unable to speak or signal traditionally.

A review of related work highlights gaps such as limited real-time performance, scalability, dataset diversity, privacy concerns, and environmental adaptability in existing systems. The paper emphasizes the potential of this integrated approach to transform emergency responses, reduce reaction times, and improve personalized aid, while noting the need for further refinement in diverse conditions.

Finally, the paper notes growing research trends toward real-time, secure identification systems and suggests future directions, including hybrid deep learning models and blockchain for secure medical data retrieval.

Conclusion

In emergencies, everysecond counts. Whetherit’samedical crisis,afire,oralife-threateningsituation,theabilitytoquickly communicate one’s needs can make all the difference. This review paper introduces a novel system that combines hand gesturerecognitionandfacialrecognitiontoaddressthiscritical need[1].Byleveragingadvancedcomputervisionandmachine learning techniques, the system identifies predefined hand gestures to signal emergencies, while also recognizing the individual’sfacetoretrievetheirmedicalhistory[2].Imaginea scenario where a person in distress can simply raise a hand gesture, and responders instantly know not only the type of emergency but also the person’s health condition—this is the future this project envisions [3].

References

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Copyright

Copyright © 2025 Utkarsh Awasthi, Prakhar Gupta, Ashutosh Singh, Vishakha Chauhan . 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.