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

arXiv: https://arxiv.org/abs/2509.11789 · HTML: https://mlsanity.com/paper/2509.11789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023)
- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks](https://mlsanity.com/paper/2304.06335/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity](https://mlsanity.com/paper/2608.13197/) (2026)

## Abstract (arXiv)

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.