A Comprehensive Survey of Fall Risk Prediction: Merging Wearable Sensors and Machine Learning

@article{1707809,
      author = {Shaistha Khanum, Siddhi Singh Rathor, Taufiq Ahmed, Syed Rayyan Ahmed, Arshiya Fathima},
      title = {A Comprehensive Survey of Fall Risk Prediction: Merging Wearable Sensors and Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {240-243},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707809.pdf},
      abstract = {Research on fall risk prediction is crucial for lowering injuries, especially in older folks and those with mobility impairments. Wearable sensors and machine learning have greatly improved the accuracy and real-time use of fall risk assessment systems. This review paper offers a comprehensive overview of recent advancements in fall risk prediction, with a focus on the collaboration of wearable sensor technology and machine learning algorithms. We investigate several sensor modalities, data preparation methods, and machine learning algorithms to detect and forecast falls. We also look at the problems, limitations, and objectives of future research in this area. By combining cutting-edge techniques, this work seeks to provide insights into the creation of trustworthy, efficient, and real-time fall risk prediction systems.},
      keywords = {Artificial Intelligence, Healthcare Technology, Fall Prevention, Wearable Sensors.},
      month = {April},
  }