This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (3464) Artificial Intelligence (2129) Falls and Emergency Detection with Sensors and other Technologies (57) Falls Prevention in the Elderly (101) New Methods (1134) Wearable Devices and Sensors (1133) Machine Learning (2076)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56750, first published .
An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design

Authors of this article:

Jinxi Zhang1, 2 Author Orcid Image ;   Zhen Li3 Author Orcid Image ;   Yu Liu4 Author Orcid Image ;   Jian Li4 Author Orcid Image ;   Hualong Qiu4 Author Orcid Image ;   Mohan Li4 Author Orcid Image ;   Guohui Hou5, 6 Author Orcid Image ;   Zhixiong Zhou2 Author Orcid Image
Article Authors Cited by (13) Tweetations Metrics

Journals

  1. Li Y, Liu P, Fang Y, Wu X, Xie Y, Xu Z, Ren H, Jing F. A Decade of Progress in Wearable Sensors for Fall Detection (2015–2024): A Network-Based Visualization Review. Sensors 2025;25(7):2205 View
  2. Inturi A, Manikandan V, Hu Y. Technical insights into vision-based fall detection systems: performances, challenges, and constraints. AI & SOCIETY 2025;40(8):6683 View
  3. J. S, L. A. An explainable artificial intelligence driven fall system for sensor data analysis enhanced by butterworth filtering. Engineering Applications of Artificial Intelligence 2025;158:111364 View
  4. Sun Y, Chen J, Ji M, Li X. Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map. Journal of Medical Internet Research 2025;27:e69077 View
  5. Yasmin A, Mahmud T, Haque S, Alamgeer S, Ngu A. Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study. Sensors 2025;25(17):5249 View
  6. Ahirwar M, Soni V. TCN-BiSRU-V2 fall detection model with performance evaluation and comparative analysis. Discover Computing 2025;28(1) View
  7. Dao T, Tran D, Bui V, Son Nguyen V, Hoa D, Van Thanh P, Tran D. RFAR: A Real-Time Firefighter Activity Recognition System Using Wearable Accelerometer. IEEE Sensors Journal 2025;25(17):33674 View
  8. Nawaz A, Abu Ali N, Ahmad A. Fall detection system using tabular GAN for data augmentation with integration of isolation forest model. Applied Soft Computing 2025;185:113931 View
  9. Rehouma H, Boukadoum M. Fall Detection by Deep Learning-Based Bimodal Movement and Pose Sensing with Late Fusion. Sensors 2025;25(19):6035 View
  10. Baek J, Li Y, Lim L, Chong J. An Interpretable AI for Smart Homes: Identifying Fall Prevention Strategies for Older Adults Using Multimodal Deep Learning. IEEE Journal of Biomedical and Health Informatics 2025;29(10):7643 View
  11. Owusu E, Acquah I, Asare M, Yeboah B. LiteFallNet: A lightweight deep learning model for efficient real-time fall detection. DIGITAL HEALTH 2025;11 View

Conference Proceedings

  1. Ciuffreda I, Casaccia S, Revel G. 2025 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT). An Ultrasonic-based Metrological Approach for Fall Detection and User recognition using Supervised and Unsupervised Machine Learning Techniques View
  2. Fasha Aqillah M, Anugrah Cahyadi W, Mukhtar H, Indriyanto S, Setiyadi S. 2025 IEEE International Conference on Artificial Intelligence for Learning and Optimization (ICoAILO). Optimising Real-Time Fall Detection: A Comparative Study of Machine Learning Algorithms Using IMU Sensor View