Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction

Eugenia Tîrziu, Ana-Mihaela Vasilevschi, Adriana Alexandru () and Eleonora Tudora ()
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Eugenia Tîrziu: National Institute for Research and Development in Informatics, 011455 Bucharest, Romania
Ana-Mihaela Vasilevschi: National Institute for Research and Development in Informatics, 011455 Bucharest, Romania
Adriana Alexandru: National Institute for Research and Development in Informatics, 011455 Bucharest, Romania
Eleonora Tudora: National Institute for Research and Development in Informatics, 011455 Bucharest, Romania

Future Internet, 2025, vol. 17, issue 8, 1-26

Abstract: In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications.

Keywords: fall detection; YOLOv11; elderly care; real-time monitoring; pose estimation; voice assistant; AI for health (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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