Toward explainable AI-empowered cognitive health assessment
Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-03-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1024195/full |
| _version_ | 1811157843245531136 |
|---|---|
| author | Abdul Rehman Javed Abdul Rehman Javed Habib Ullah Khan Mohammad Kamel Bader Alomari Muhammad Usman Sarwar Muhammad Asim Ahmad S. Almadhor Muhammad Zahid Khan |
| author_facet | Abdul Rehman Javed Abdul Rehman Javed Habib Ullah Khan Mohammad Kamel Bader Alomari Muhammad Usman Sarwar Muhammad Asim Ahmad S. Almadhor Muhammad Zahid Khan |
| author_sort | Abdul Rehman Javed |
| collection | DOAJ |
| description | Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively. |
| first_indexed | 2024-04-10T05:14:17Z |
| format | Article |
| id | doaj.art-e5927e84b35b43c8bff77502c84fb5ca |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-10T05:14:17Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-e5927e84b35b43c8bff77502c84fb5ca2023-03-09T05:08:03ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-03-011110.3389/fpubh.2023.10241951024195Toward explainable AI-empowered cognitive health assessmentAbdul Rehman Javed0Abdul Rehman Javed1Habib Ullah Khan2Mohammad Kamel Bader Alomari3Muhammad Usman Sarwar4Muhammad Asim5Ahmad S. Almadhor6Muhammad Zahid Khan7Department of Cyber Security, Air University, Islamabad, PakistanDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonDepartment of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, QatarDepartment of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, QatarDepartment of Computer Games Development, Air University, Islamabad, PakistanDepartment of Cyber Security, National University of Computer and Emerging Science, Islamabad, PakistanCollege of Computer and Information Sciences, Jouf University, Sakakah, Saudi ArabiaDepartment of Computer Science & IT, University of Malakand, Chakdara, PakistanExplainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1024195/fullexplainable AIadvanced sensorsassistive technologykey feature extractionhuman activity recognitionInternet of Things |
| spellingShingle | Abdul Rehman Javed Abdul Rehman Javed Habib Ullah Khan Mohammad Kamel Bader Alomari Muhammad Usman Sarwar Muhammad Asim Ahmad S. Almadhor Muhammad Zahid Khan Toward explainable AI-empowered cognitive health assessment Frontiers in Public Health explainable AI advanced sensors assistive technology key feature extraction human activity recognition Internet of Things |
| title | Toward explainable AI-empowered cognitive health assessment |
| title_full | Toward explainable AI-empowered cognitive health assessment |
| title_fullStr | Toward explainable AI-empowered cognitive health assessment |
| title_full_unstemmed | Toward explainable AI-empowered cognitive health assessment |
| title_short | Toward explainable AI-empowered cognitive health assessment |
| title_sort | toward explainable ai empowered cognitive health assessment |
| topic | explainable AI advanced sensors assistive technology key feature extraction human activity recognition Internet of Things |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1024195/full |
| work_keys_str_mv | AT abdulrehmanjaved towardexplainableaiempoweredcognitivehealthassessment AT abdulrehmanjaved towardexplainableaiempoweredcognitivehealthassessment AT habibullahkhan towardexplainableaiempoweredcognitivehealthassessment AT mohammadkamelbaderalomari towardexplainableaiempoweredcognitivehealthassessment AT muhammadusmansarwar towardexplainableaiempoweredcognitivehealthassessment AT muhammadasim towardexplainableaiempoweredcognitivehealthassessment AT ahmadsalmadhor towardexplainableaiempoweredcognitivehealthassessment AT muhammadzahidkhan towardexplainableaiempoweredcognitivehealthassessment |