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research-article

A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people

Authors:

Lieyun DingAuthors Info & Claims

Volume 252, Issue C

https://doi.org/10.1016/j.knosys.2022.109351

Published: 27 September 2022 Publication History

Abstract

With a rapidly aging population, the health problems of older individuals have attracted increasing attention. Elderly people are exposed to more health risks, and their behavior can often indicate signs of crises and diseases. This paper proposes a method for multiclass abnormal behavior recognition based on the integration of sensors (accelerometer, gyroscope, and orientation sensor) in smartphones with positioning monitoring systems. An attention-convolutional neural network (CNN)-long short-term memory (LSTM) algorithm is introduced for human action recognition, which can handle time-dependent data with multiple features of varying importance. Furthermore, based on the long-term activity data (action and position) of the human body, a hidden Markov model (HMM) of the individual’s daily behavior activity state is constructed. Experimental results show that compared with the existing approaches, the proposed attention-CNN-LSTM algorithm performs better in recognizing different human behaviors, with 94.2% precision, 95.1% recall, and F 1-score of 94.6%. The developed daily behavior HMM for an individual has been proven to be able to detect changes in human behavioral patterns and indicate the specific behaviors that have changed. The method proposed in this paper can provide a powerful technical guarantee for the health and care of elderly individuals at home.

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Cited By

Chen LJiang XWang Y(2024)A Bayesian network learning method for sparse and unbalanced data with GNN-based multilabel classification applicationApplied Soft Computing10.1016/j.asoc.2024.111393154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111393
Zekri DSnoun ADelot TThilliez M(2024)A Deep Learning Based System For a Long-term Elderly Behavioral Drift DetectionSN Computer Science10.1007/s42979-024-03207-35:7Online publication date: 27-Sep-2024
https://dl.acm.org/doi/10.1007/s42979-024-03207-3

Index Terms

A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 252, Issue C

Sep 2022

1049 pages

ISSN:0950-7051

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 27 September 2022

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Cited By

Chen LJiang XWang Y(2024)A Bayesian network learning method for sparse and unbalanced data with GNN-based multilabel classification applicationApplied Soft Computing10.1016/j.asoc.2024.111393154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111393
Zekri DSnoun ADelot TThilliez M(2024)A Deep Learning Based System For a Long-term Elderly Behavioral Drift DetectionSN Computer Science10.1007/s42979-024-03207-35:7Online publication date: 27-Sep-2024
https://dl.acm.org/doi/10.1007/s42979-024-03207-3

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