LSTM 아키텍쳐를 활용한 낙상 감지 모델 연구: 영상 데이터 기반 접근
Fall Detection Model Using LSTM Architecture: A Video Data-Based Approach
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (19)
- "Google Research"[URL]
- Han, D. H., "A Study on the Fall Detection Algorithm Based on Accelerometer Sensor", Journal of IT Policy and Management, 12(6), pp.2139~2144 (2020)
- Hochreiter, S., "Long Short-Term Memory", Neural Computation, 9(8), pp.1735~1780 (1997)
- Hong, M. K., "Proposal of an Improved Fall Detection Using GRU", Journal of the Korea Information Processing Society, 30(2), pp.287~288 (2023)
- Jeong, S. S., "Study of regularization of long short-term memory(LSTM)for fall detection system of the elderly", Journal of the Korea Institute of Information and Communication Engineering, 25(11), pp.1649~1654 (2021)
- Kim, S. K., "Implementation of Fall-Down Detection Algorithm Based on Image Processing", Journal of Satellite Information and Communication, 12(2), pp.56~60 (2017)
- Kim, Y. J., "Development of Fall Detection Model based on Machine Learning Algorithm", Journal of The Korean Institute of Communications and Information Sciences, pp.905~908 (2023)
- Korea Disease Control and Prevention Agency, "2021Regional Trauma-Based Severe Trauma Investigation Statistics", ~ (2021)
- 2020 Elderly Survey Report, "Korea Institute for Health and Social Affairs", Ministry of Health and Welfare, ~ (2020)
- Lim, D. H., "Fall Detection System Using 3-Axial Acceleration", Proceedings of the Korea Information Processing Society Conference, 20(1), pp.356~358 (2013)
- Mastorakis, G., "Fall detection system using Kinect’s infrared sensor", Journal of Real-Time Image Processing, 9, pp.635~646 (2014)
- Increase in Falls Due to Aging and Social Environment Changes, "Ministry of the Interior and Safety", National Disaster Management Research Institute, ~ (2023)
- Park, W. Y., "A Study on the Deep Learning-Based Fall Detection Using Camera", Proceedings of the Korea Next Generation Computing Society Conference, pp.402~404 (2022)
- Patel, S., "A review of wearable sensors and systems with application in rehabilitation", Journal of Neuroengineering and Rehabilitation, 9, pp.1~17 (2012)
- Redmon, J., "You Only Look Once: Unified, Real-Time Object Detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.779~788 (2016)
- Shoaib, M., "A survey of online activity recognition using mobile phones", Sensors, 15(1), pp.2059~2085 (2015)
- Statistics Korea, "2023 Senior Statistics", ~ (2023)
- Welch, G., "An Introduction to the Kalman Filter", ~ (1995)