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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.11789 |
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| _version_ | 1866914038166847488 |
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| author | Aderinola, Timilehin B. Palmerini, Luca D'Ascanio, Ilaria Chiari, Lorenzo Klenk, Jochen Becker, Clemens Caulfield, Brian Ifrim, Georgiana |
| author_facet | Aderinola, Timilehin B. Palmerini, Luca D'Ascanio, Ilaria Chiari, Lorenzo Klenk, Jochen Becker, Clemens Caulfield, Brian Ifrim, Georgiana |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Watch Your Step: A Cost-Sensitive Framework for Accelerometer-Based Fall Detection in Real-World Streaming Scenarios Aderinola, Timilehin B. Palmerini, Luca D'Ascanio, Ilaria Chiari, Lorenzo Klenk, Jochen Becker, Clemens Caulfield, Brian Ifrim, Georgiana Machine Learning 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. |
| title | Watch Your Step: A Cost-Sensitive Framework for Accelerometer-Based Fall Detection in Real-World Streaming Scenarios |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11789 |