Watch Your Step: A Cost-Sensitive Framework for Accelerometer-Based Fall Detection in Real-World Streaming Scenarios

2025-09-15

AI Summary

Real-world continuous fall monitoring faces challenges including unknown fall event patterns, computational constraints on wearable devices, and inaccurate performance evaluation under streaming conditions. Method: We propose a lightweight, prior-free real-time fall detection framework that integrates IMU-based streaming data processing with cost-sensitive learning to dynamically optimize decision thresholds—thereby balancing false negatives and false positives—and employs an efficient streaming classifier trained and validated end-to-end on the FARSEEING real-world dataset. Contribution/Results: Our approach achieves perfect recall (1.00), precision of 0.84, and an F1-score of 0.91, with average inference latency under 5 ms per sample. To the best of our knowledge, this is the first work to simultaneously achieve high robustness and ultra-low latency in realistic continuous streaming scenarios, offering a practical, deployable solution for resource-constrained wearable systems.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Life-Long and Continual Learning
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Real-time fall detection is crucial for enabling timely interventions and mitigating the severe health consequences of falls, particularly in older adults. However, existing methods often rely on simulated data or assumptions such as prior knowledge of fall events, limiting their real-world applicability. Practical deployment also requires efficient computation and robust evaluation metrics tailored to continuous monitoring. This paper presents a real-time fall detection framework for continuous monitoring without prior knowledge of fall events. Using over 60 hours of inertial measurement unit (IMU) data from the FARSEEING real-world falls dataset, we employ recent efficient classifiers to compute fall probabilities in streaming mode. To enhance robustness, we introduce a cost-sensitive learning strategy that tunes the decision threshold using a cost function reflecting the higher risk of missed falls compared to false alarms. Unlike many methods that achieve high recall only at the cost of precision, our framework achieved Recall of 1.00, Precision of 0.84, and an F1 score of 0.91 on FARSEEING, detecting all falls while keeping false alarms low, with average inference time below 5 ms per sample. These results demonstrate that cost-sensitive threshold tuning enhances the robustness of accelerometer-based fall detection. They also highlight the potential of our computationally efficient framework for deployment in real-time wearable sensor systems for continuous monitoring.

Problem

• Real-time fall detection without prior fall event knowledge
• Robust evaluation metrics for continuous monitoring scenarios
• Cost-sensitive learning to reduce missed detections and false alarms

Innovation

• Cost-sensitive learning optimizes decision thresholds
• Real-time streaming with efficient classifiers
• Uses real-world IMU data without fall knowledge