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Communication Dans Un Congrès Année : 2023

Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data

Apprentissage automatique et classement des caractéristiques pour la détection d'impact d'une chute en utilisant des données multisensorielles

Résumé

Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.
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Dates et versions

hal-04390227 , version 1 (12-01-2024)

Identifiants

Citer

Tresor Koffi, Youssef Mourchid, Mohammed Hindawi, Yohan Dupuis. Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data. 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Sep 2023, Poitiers, France. pp.1-6, ⟨10.1109/MMSP59012.2023.10337682⟩. ⟨hal-04390227⟩
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