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Erschienen in:
Open Source Implementation for Fall Classification and Fall Detection Systems Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Challenges and Trends in Multimodal Fall Detection for Healthcare

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches
Nächstes Kapitel Multi-sensor System, Gamification, and Artificial Intelligence for Benefit Elderly People
Fußnoten
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
An extra activity has been labeled “kneeling” (20) when a subject remains on his knees after falling [44].
 
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
useKernelEstimator=false, debug=false, displayModelInOldFormat=false, doNotCheckCapabilities=false, useSupervisedDiscretization=false.
 
5
estimator=SimpleEstimator, debug=false, searchAlgorithm=F2, doNotCheckCapabilities=false, useADTree=false.
 
6
seed=1, allowUnclassifiedInstances=false, debug=false, minNum=1.0, numFolds=0, doNotCheckCapabilities=false, maxDepth=0, minVarianceProp=0.001, KValue=0.
 
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
Gaussian Kernel activation Function, 10 hidden neurons.
 
9
The model will have the same parameter setting used in the FNN.
 
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Metadaten
Titel
An Interpretable Machine Learning Model for Human Fall Detection Systems Using Hybrid Intelligent Models
verfasst von
Paulo Vitor C. Souza
Augusto J. Guimaraes
Vanessa S. Araujo
Lucas O. Batista
Thiago S. Rezende
Copyright-Jahr
2020
Buch
Challenges and Trends in Multimodal Fall Detection for Healthcare

Print ISBN: 978-3-030-38747-1

Electronic ISBN: 978-3-030-38748-8

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-38748-8_8

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