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An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models

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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

This chapter presents an assessment of falls and everyday situations in people by sensors dataset collected in fall simulation. This evaluation was performed through the use of intelligent techniques and models based on feature selection techniques and fuzzy neural networks. Therefore, this work can be seen as an auxiliary approach of presenting a vision of knowledge extraction for the construction of actions, prevention, and training to functional that will work in areas correlated to health impacts of people who may have difficulties or injuries due to the impact suffered in a fall. The results obtained were compared with state of the art for the theme and the version of the hybrid model that acts on the most relevant dataset dimensions identifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people.

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Notes

  1. 1.

    Fall detection using data only from wearable IMUs. This experiment was replicated due to the fuzzy neural network constraint working with numerical data only.

  2. 2.

    An extra activity has been labeled “kneeling” (20) when a subject remains on his knees after falling [44].

  3. 3.

    M=3, bt=8, \( \gamma = 70\% \). For a preliminary 10-k-fold test for M = [3, 4, 5], bt = [4, 8, 16] and \( \gamma = [50\%, 60\%, 70\%] \).

  4. 4.

    useKernelEstimator=false, debug=false, displayModelInOldFormat=false, doNotCheckCapabilities=false, useSupervisedDiscretization=false.

  5. 5.

    estimator=SimpleEstimator, debug=false, searchAlgorithm=F2, doNotCheckCapabilities=false, useADTree=false.

  6. 6.

    seed=1, allowUnclassifiedInstances=false, debug=false, minNum=1.0, numFolds=0, doNotCheckCapabilities=false, maxDepth=0, minVarianceProp=0.001, KValue=0.

  7. 7.

    seed=1, unpruned=false, confidenceFactor=0.25, numFolds=3, reducedErrorPruning=false, useLaplace=false, doNotMakeSplitPointActualValue=false, debug=false, subtreeRaising=true, saveInstanceData=false, binarySplits=false, doNotCheckCapabilities=false, minNumObj=2, useMDLcorrection=true.

  8. 8.

    Gaussian Kernel activation Function, 10 hidden neurons.

  9. 9.

    The model will have the same parameter setting used in the FNN.

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Acknowledgements

The acknowledgments of this work go to Faculdade Una de Betim, the Federal Center for Technological Education of Minas Gerais and Professor Hiram Ponce, who trusted our work and invited us to contribute his book.

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Authors and Affiliations

  1. Faculty UNA of Betim. Av. Gov. Valadares, 640 Centro, Betim, MG, 32510-010, Brazil

    Paulo Vitor C. Souza, Augusto J. Guimaraes, Vanessa S. Araujo, Lucas O. Batista & Thiago S. Rezende

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  1. Paulo Vitor C. Souza
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  2. Augusto J. Guimaraes
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  3. Vanessa S. Araujo
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Correspondence to Paulo Vitor C. Souza .

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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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Souza, P.V.C., Guimaraes, A.J., Araujo, V.S., Batista, L.O., Rezende, T.S. (2020). An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models. 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_8

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