Real-time Fall Monitoring System for the Elderly Based on Multimodal Sensor Fusion
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Open Access
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Real-time Fall Monitoring System for the Elderly Based on Multimodal Sensor Fusion

Wanning Chen 1*
1 Yanshan University
*Corresponding author: chenwanning123456789@outlook.com
Published on 3 September 2025
Journal Cover
TNS Vol.134
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-343-7
ISBN (Online): 978-1-80590-344-4
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Abstract

With the acceleration of the global aging process, falls among the elderly have become a major public health issue threatening their health. Currently, the single sensor monitoring technology has significant limitations: the misjudgment rate of wearable accelerometers for daily activities, visual monitoring is significantly affected by light and there is a potential risk of privacy leakage, making it difficult to adapt to complex home scenarios. This paper reviews the research progress of real - time fall monitoring systems for the elderly based on multimodal sensor fusion, focuses on analyzing the collaborative mechanisms of millimeter - wave radar, accelerometers, and heart rate sensors, and summarizes key technologies such as data fusion architectures, algorithm optimization, and edge computing deployment. By comparing the performance differences of different fusion strategies, it is found that the three - level attention fusion architecture performs best in complex scenarios. At the same time, this paper points out problems in current research, such as the insufficient proportion of open - source data in home scenarios and the lack of night - time monitoring solutions, and looks forward to the future development direction of combining the Transformer architecture with privacy computing.

Keywords:

Elderly Fall Monitoring, Multimodal Sensor Fusion, Real-time Early Warning, Edge Computing, Privacy Protection

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Chen,W. (2025). Real-time Fall Monitoring System for the Elderly Based on Multimodal Sensor Fusion. Theoretical and Natural Science,134,31-37.

References

[1]. National Health Commission. (2023). White Paper on Elderly Health in China. Beijing: People's Medical Publishing House.

[2]. World Health Organization. (2022). Global Report on Falls Prevention in Older Age. Geneva: WHO Press.

[3]. Zhang, M., et al. (2021). Analysis of the limitations of visual fall detection in home environments. Acta Automatica Sinica, 47(8), 1765-1774.

[4]. Chen, Z. Q., Liu, W., Zhao, Y. (2023). A survey of fall detection technologies for the elderly: from single sensor to multimodal fusion. IEEE Transactions on Instrumentation and Measurement, 72, 1-13.

[5]. Wang, X. D., Liu, C. (2020). Application status of multi-sensor fusion technology in elderly fall monitoring. Sensors and Microsystems, 39(3), 1-4.

[6]. Liu, Y., Zhou, M., Wu, H. (2023). Fall detection technology of millimeter-wave radar in complex home environments. Journal of Electronics & Information Technology, 45(7), 2013-2021.

[7]. Zheng, X. W., Sun, P., Li, J. (2023). Feature extraction and correlation analysis of heart rate variability in elderly fall events. Chinese Journal of Biomedical Engineering, 42(3), 289-298.

[8]. Huang, H., et al. (2022). Real-time fall detection system for the elderly based on edge computing. Journal of Computer Applications, 42(5), 1567-1574.

[9]. Anguita, D., Ghio, A., Oneto, L., et al. (2013). A public domain dataset for human activity recognition using smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 1-6.

[10]. Fall Detection Dataset. (2020). Kaggle.  https: //www. kaggle.com/datasets.

[11]. National Bureau of Statistics. (2024). China Statistical Yearbook 2024. Beijing: China Statistics Press.

[12]. Smith, J., Lee, K., Park, S. (2023). Nighttime fall detection: Challenges and dataset augmentation strategies. IEEE Journal of Biomedical and Health Informatics, 27(4), 1890-1898.

[13]. Peking Union Medical College Hospital. (202 2). Report on the Evaluation of the Clinical Application Effect of the Multimodal Fall Monitoring System. Beijing: Peking Union Medical College Hospital.

[14]. GDPR. (2016). Article 25: Data minimiza tion.  https: //gdpr-info.eu/art-25-gdpr/.

References

[1]. National Health Commission. (2023). White Paper on Elderly Health in China. Beijing: People's Medical Publishing House.

[2]. World Health Organization. (2022). Global Report on Falls Prevention in Older Age. Geneva: WHO Press.

[3]. Zhang, M., et al. (2021). Analysis of the limitations of visual fall detection in home environments. Acta Automatica Sinica, 47(8), 1765-1774.

[4]. Chen, Z. Q., Liu, W., Zhao, Y. (2023). A survey of fall detection technologies for the elderly: from single sensor to multimodal fusion. IEEE Transactions on Instrumentation and Measurement, 72, 1-13.

[5]. Wang, X. D., Liu, C. (2020). Application status of multi-sensor fusion technology in elderly fall monitoring. Sensors and Microsystems, 39(3), 1-4.

[6]. Liu, Y., Zhou, M., Wu, H. (2023). Fall detection technology of millimeter-wave radar in complex home environments. Journal of Electronics & Information Technology, 45(7), 2013-2021.

[7]. Zheng, X. W., Sun, P., Li, J. (2023). Feature extraction and correlation analysis of heart rate variability in elderly fall events. Chinese Journal of Biomedical Engineering, 42(3), 289-298.

[8]. Huang, H., et al. (2022). Real-time fall detection system for the elderly based on edge computing. Journal of Computer Applications, 42(5), 1567-1574.

[9]. Anguita, D., Ghio, A., Oneto, L., et al. (2013). A public domain dataset for human activity recognition using smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 1-6.

[10]. Fall Detection Dataset. (2020). Kaggle.  https: //www. kaggle.com/datasets.

[11]. National Bureau of Statistics. (2024). China Statistical Yearbook 2024. Beijing: China Statistics Press.

[12]. Smith, J., Lee, K., Park, S. (2023). Nighttime fall detection: Challenges and dataset augmentation strategies. IEEE Journal of Biomedical and Health Informatics, 27(4), 1890-1898.

[13]. Peking Union Medical College Hospital. (202 2). Report on the Evaluation of the Clinical Application Effect of the Multimodal Fall Monitoring System. Beijing: Peking Union Medical College Hospital.

[14]. GDPR. (2016). Article 25: Data minimiza tion.  https: //gdpr-info.eu/art-25-gdpr/.

Cite this article

Chen,W. (2025). Real-time Fall Monitoring System for the Elderly Based on Multimodal Sensor Fusion. Theoretical and Natural Science,134,31-37.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Controlling Robotic Manipulator Using PWM Signals with Microcontrollers

ISBN: 978-1-80590-343-7(Print) / 978-1-80590-344-4(Online)
Editor: Marwan Omar, Mustafa Istanbullu
Conference website: https://2025.confapmm.org/adana.html
Conference date: 19 September 2025
Series: Theoretical and Natural Science
Volume number: Vol.134
ISSN: 2753-8818(Print) / 2753-8826(Online)

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