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October 13, 2025Open Access

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

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This analysis presents a cost-sensitive approach to enhance fall detection in real-time scenarios, aiming to minimize false alarms.

The proposed framework achieves a recall of 1.00 and a precision of 0.84 in real-time fall detection.
Using over 60 hours of IMU data from the FARSEEING dataset, it effectively monitors falls without prior event knowledge.
The cost-sensitive learning strategy tunes the decision threshold to reduce missed falls compared to false alarms.
Average inference time remains under 5 ms per sample, emphasizing the framework's efficiency for wearable sensor systems.

Aderinola et al. (2025) studied this question.

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