Comprehensive Analysis of IoMT-Based Fall Detection and Prediction System Using Machine Learning Models

Abstract

Falls represent one of the most critical health hazards for elderly populations globally, accounting for significant morbidity, mortality, and healthcare expenditure. The Internet of Medical Things (IoMT) has emerged as a transformative paradigm that interconnects medical devices, sensors, and analytical platforms to enable continuous, real-time health monitoring. This paper presents a comprehensive analysis of IoMT-based fall detection and prediction systems leveraging machine learning (ML) models, examining the state-of-the-art methodologies, sensor modalities, algorithmic frameworks, and deployment challenges that define this rapidly evolving field. We systematically review wearable inertial measurement units (IMUs), ambient sensor networks, computer vision approaches, and hybrid sensor fusion strategies, assessing their efficacy in the context of fall detection and proactive fall risk prediction. A detailed survey of ML techniques—spanning classical algorithms such as Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbors (k-NN), to deep learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Transformer-based models—is conducted to evaluate accuracy, latency, and resource constraints. We further analyze edge computing and federated learning strategies for privacy-preserving, low-latency deployment in clinical and home environments. Challenges including dataset scarcity, class imbalance, interoperability, and real-world generalizability are critically examined. The paper concludes with a synthesis of open research directions, including multimodal fusion, explainable AI integration, and standardized benchmarking, providing a roadmap for advancing the next generation of intelligent fall management systems.