Purpose: This study aimed to provide a detailed understanding of nurses’ experiences with fall management in wards equipped with a video-based fall detection system. Methods: In-depth, semi-structured interviews were conducted with 10 nurses from an integrated nursing care ward at K Hospital in City C, where the system had been implemented. The interviews focused on nurses’ actual experiences and reflections regarding fall management. Data were systematically analyzed using Hsieh and Shannon’s conventional content analysis, which identified meaningful categories and themes. Results: The analysis identified six themes and 15 subthemes. The main themes were: Context of falls and limitations in management falls occurred through interactions between patient behaviors and environmental factors, while current assessment and management systems did not adequately address these complexities. Need for structured response processes after introducing video-based fall detection although video-based systems were implemented, fall recognition and responses remained experience-based and situation-dependent, highlighting the need for standardized, systematic procedures. Perceived limitations of video-based fall detection systems the system presented challenges such as delayed and false alarms, which reduced real-time responsiveness and affected clinical reliability. Practical benefits of video-based fall management and changes in nursing practice video verification improved the objectivity and accuracy of fall reporting, enhancing the consistency and systematization of nursing practice. Strategies for system use according to ward environment tailored use of the system based on ward characteristics and patient composition was suggested to optimize monitoring efficiency and fall prevention. Future directions for improved fall management strategies to enhance patient and caregiver awareness through video-based education and to improve ward environments were proposed as approaches for developing a preventive, smart-care model. Conclusion: The findings of this study indicate future directions and challenges for technology-based nursing practice in fall management, highlighting the need to develop new assessment frameworks, as well as educational and research strategies that reflect nurses’ experiences in diverse contexts, given the practical changes introduced by the video-based fall detection system and the limitations of current assessment tools.

The purpose of this study is to develop a timely fall detection system aimed at improving elderly care, reducing injury risks, and promoting greater independence among older adults. Falls are a leading cause of severe complications, long-term disabilities, and even mortality in the aging population, making their detection and prevention a crucial area of public health focus. This research introduces an innovative fall detection approach by leveraging Mediapipe, a state-of-the-art computer vision tool designed for human posture tracking. By analyzing the velocity of keypoints derived from human movement data, the system is able to detect abrupt changes in motion patterns, which are indicative of potential falls. To enhance the accuracy and robustness of fall detection, this system integrates an LSTM (Long Short-Term Memory) model specifically optimized for time-series data analysis. LSTM's ability to capture critical temporal shifts in movement patterns ensures the system's reliability in distinguishing falls from other types of motion. The combination of Mediapipe and LSTM provides a highly accurate and robust monitoring system with a significantly reduced false-positive rate, making it suitable for real-world elderly care environments. Experimental results demonstrated the efficacy of the proposed system, achieving an F1 score of 0.934, with a precision of 0.935 and a recall of 0.932. These findings highlight the system's capability to handle complex motion data effectively while maintaining high accuracy and reliability. The proposed method represents a technological advancement in fall detection systems, with promising potential for implementation in elderly monitoring systems. By improving safety and quality of life for older adults, this research contributes meaningfully to advancements in elderly care technology.

This paper presents a vision-based fall detection system to automatically monitor and detect people’s fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.