Current Volume 9
An Adaptive, Multimodal and Explainable AI Framework for Predictive and Personalized Assistive Technology in Elderly Independent Living
Abstract
The rapid growth of the elderly population has increased the demand for intelligent healthcare systems that can support safe independent living. Senior citizens often require continuous monitoring because of age-related health conditions, reduced mobility, and difficulty in performing daily activities. This work presents an adaptive and multimodal AI-based elderly monitoring framework designed for predictive and personalized assistive healthcare applications. The developed framework combines physiological, behavioral, and environmental parameters including heart rate, blood pressure, motion level, room temperature, light intensity, step count, speech commands, behavioral condition, and interaction status. A Random Forest classification model is employed to recognize and predict activities such as Sitting, Standing, and Walking using multimodal healthcare data. To improve classification performance and prediction reliability, a realistic and balanced healthcare dataset was generated and merged with the original dataset. Experimental evaluation demonstrated improved prediction accuracy and stable cross-validation performance after dataset enhancement and preprocessing. A multilingual graphical user interface was also integrated to improve accessibility for elderly users who may not be comfortable interacting in English. Regional language support increases usability and makes the framework more inclusive for elderly individuals from different language backgrounds. The proposed system can further be extended using wearable sensors, IoT-based monitoring, and Explainable AI techniques for future smart healthcare environments.
Keywords
Elderly Monitoring, Explainable AI, Human Activity Recognition, Random Forest, Smart Healthcare, Multilingual Interface, Assistive Technology.
Citations
IRE Journals:
Kusuma Puttaswamy, Prof. Rakshitha B S "An Adaptive, Multimodal and Explainable AI Framework for Predictive and Personalized Assistive Technology in Elderly Independent Living" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2456-2465 https://doi.org/10.64388/IREV9I11-1717907
IEEE:
Kusuma Puttaswamy, Prof. Rakshitha B S
"An Adaptive, Multimodal and Explainable AI Framework for Predictive and Personalized Assistive Technology in Elderly Independent Living" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717907
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12