Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul 7:2023:5684914.
doi: 10.1155/2023/5684914. eCollection 2023.

Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Shtwai Alsubai  1 , Abdullah Alqahtani  1 , Mohemmed Sha  1 , Sidra Abbas  2 , Michal Gregus  3 , Robert Furda  3
Affiliations

Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Shtwai Alsubai et al. Comput Intell Neurosci. .

Abstract

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96% accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Proposed approach for dementia detection.
Figure 2
Figure 2
Result of decision tree classifier for dementia detection individuals. (a) Confusion matrix. (b) ROC curve.
Figure 3
Figure 3
Result of naive bayes classifier for dementia detection individuals. (a) Confusion matrix. (b) ROC curve.
Figure 4
Figure 4
Result of SVM classifier for dementia detection individuals. (a) Confusion matrix. (b) ROC curve.
Figure 5
Figure 5
Result of MLP classifier for dementia detection individuals. (a) Confusion matrix (b) Roc curve.
Figure 6
Figure 6
Result of DNN classifier for dementia detection individuals. (a) Accuracy curve (b) loss curve.
See this image and copyright information in PMC

References

    1. Pliatsikas P., Economides A. A. Factors influencing intention of Greek consumers to use smart home technology. Applied System Innovation . 2022;5(1):p. 26. doi: 10.3390/asi5010026. - DOI
    1. Li W., Yigitcanlar T., Erol I., Liu A. Motivations, barriers and risks of smart home adoption: from systematic literature review to conceptual framework. Energy Research & Social Science . 2021;80 doi: 10.1016/j.erss.2021.102211.102211 - DOI
    1. Javed A. R., Fahad L. G., Farhan A. A., et al. Automated cognitive health assessment in smart homes using machine learning. Sustainable Cities and Society . 2021;65 doi: 10.1016/j.scs.2020.102572.102572 - DOI
    1. Sarkar J. L., Majumder A., Majumder A., et al. I-health: sdn-based fog architecture for iiot applications in healthcare. IEEE/ACM Transactions on Computational Biology and Bioinformatics . 2022:1–8. doi: 10.1109/tcbb.2022.3193918. - DOI - PubMed
    1. Gillani L. F., Rajarajan M. Anomalies detection in smart-home activities. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA); December 2015; Miami, FL, U.S.A. IEEE; pp. 419–422.