Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition
This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study lev...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10477420/ |
| _version_ | 1827292868816928768 |
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| author | Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Stephen Ojo Ahmad S. Almadhor Abdullah Al Hejaili Lubomira Strazovska |
| author_facet | Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Stephen Ojo Ahmad S. Almadhor Abdullah Al Hejaili Lubomira Strazovska |
| author_sort | Sidra Abbas |
| collection | DOAJ |
| description | This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities. The findings have implications for proactive and personalized elderly care, representing an approach to improving prediction performance in this domain. The research experiments are presented systematically, summarizing the outcomes of various machine-learning models across three iterative experiments. This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation. This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same classifiers. Results reveal that LSTM achieved the best accuracy of 0.95% for 7 activities and 0.96% using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals. Hence, the findings presented in this study have showcased the potential to enhance the quality of life for seniors using the blend of ML, DL and AL. |
| first_indexed | 2024-04-24T13:15:21Z |
| format | Article |
| id | doaj.art-c4749961f8794a7f831b72179fb4b907 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T13:15:21Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c4749961f8794a7f831b72179fb4b9072024-04-04T23:00:22ZengIEEEIEEE Access2169-35362024-01-0112449494495910.1109/ACCESS.2024.338043210477420Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity RecognitionSidra Abbas0https://orcid.org/0009-0001-0117-4390Gabriel Avelino Sampedro1https://orcid.org/0000-0003-2354-4409Shtwai Alsubai2https://orcid.org/0000-0002-6584-7400Stephen Ojo3https://orcid.org/0000-0002-2383-621XAhmad S. Almadhor4https://orcid.org/0000-0002-8665-1669Abdullah Al Hejaili5Lubomira Strazovska6Department of Computer Science, COMSATS University Islamabad, Islamabad, PakistanFaculty of Information and Communication Studies, University of the Philippines Open University, Los Baños, PhilippinesCollege of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC, USADepartment of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi ArabiaComputer Science Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi ArabiaFaculty of Management, Comenius University in Bratislava, Bratislava, SlovakiaThis research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight into the daily activities of older individuals and AL-based ML and DL techniques to construct predictive models for these activities. The findings have implications for proactive and personalized elderly care, representing an approach to improving prediction performance in this domain. The research experiments are presented systematically, summarizing the outcomes of various machine-learning models across three iterative experiments. This research explored a diverse array of ML algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGB) and DL methods such as Deep Neural Networks (DNN) and Long Short-Term Memory networks (LSTM) for experimentation. This research trained models on 7 activities: walking, shuffling, climbing stairs (up and down), standing, sitting, and lying down, and 4 activities separately: standing, sitting, walking, and lying down, using the same classifiers. Results reveal that LSTM achieved the best accuracy of 0.95% for 7 activities and 0.96% using RF on 4 actives, showing the potential of DL and ML techniques, particularly when integrated with AL, to enhance activity recognition rate, patient care, optimize medication strategies and improve the well-being of elderly individuals. Hence, the findings presented in this study have showcased the potential to enhance the quality of life for seniors using the blend of ML, DL and AL.https://ieeexplore.ieee.org/document/10477420/Active learning (AL)elderly activity recognitionhuman activity recognition (HAR)healthcaredeep learning (DL)machine learning (ML) |
| spellingShingle | Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Stephen Ojo Ahmad S. Almadhor Abdullah Al Hejaili Lubomira Strazovska Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition IEEE Access Active learning (AL) elderly activity recognition human activity recognition (HAR) healthcare deep learning (DL) machine learning (ML) |
| title | Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition |
| title_full | Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition |
| title_fullStr | Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition |
| title_full_unstemmed | Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition |
| title_short | Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition |
| title_sort | advancing healthcare and elderly activity recognition active machine and deep learning for fine grained heterogeneity activity recognition |
| topic | Active learning (AL) elderly activity recognition human activity recognition (HAR) healthcare deep learning (DL) machine learning (ML) |
| url | https://ieeexplore.ieee.org/document/10477420/ |
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