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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

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Challenges and Trends in Multimodal Fall Detection for Healthcare

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 273))

Abstract

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.

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Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Funding

The authors declare that this work was performed as part of their employment in Universidad Panamericana (Mexico).

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Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, 20290, Aguascalientes, Aguascalientes, Mexico

    Ricardo Espinosa & Sebastián Gutiérrez

  2. Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920, Ciudad de México, Mexico

    Hiram Ponce, Lourdes Martínez-Villaseñor, Jorge Brieva & Ernesto Moya-Albor

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  1. Ricardo Espinosa
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  2. Hiram Ponce
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  3. Sebastián Gutiérrez
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  4. Lourdes Martínez-Villaseñor
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  5. Jorge Brieva
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  6. Ernesto Moya-Albor
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Correspondence to Ricardo Espinosa .

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Editors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Hiram Ponce

  2. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  3. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Jorge Brieva

  4. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Ernesto Moya-Albor

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Espinosa, R., Ponce, H., Gutiérrez, S., Martínez-Villaseñor, L., Brieva, J., Moya-Albor, E. (2020). Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras. In: Ponce, H., Martínez-Villaseñor, L., Brieva, J., Moya-Albor, E. (eds) Challenges and Trends in Multimodal Fall Detection for Healthcare. Studies in Systems, Decision and Control, vol 273. Springer, Cham. https://doi.org/10.1007/978-3-030-38748-8_5

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