Volume 1 - Issue 2, May - June 2026

This method explores the application of sophisticated computational methods and sensor data. Deep learning (DL), machine learning (ML), and active learning (AL) are methods for identifying everyday activities among the elderly. Using the HAR70+ dataset, which captures activity patterns of older individuals, seven activities—walking, shuffling, climbing stairs, standing, sitting, and lying down—as well as a subset of four essential activities—standing, sitting, walking, and lying down—are identified by the study using predictive models. Multiple ML algorithms (Random Forest, XGBoost, Logistic Regression, KNN, Stochastic Gradient Descent) and DL techniques (Deep Neural Networks and LSTM) were used in three experiments. LSTM achieved the highest accuracy of 95% for seven activities, while Random Forest reached 96% for the four-activity subset. These results highlight the effectiveness of integrating AL with ML and DL to improve activity recognition, personalize elderly care, enhance medication planning, and support overall well-being.

Somala Rama Kishore, Malothu Amru, Dr. Mikkili. Dileep Kumar, Edem Suresh Babu, Bodla Kishor,"AI-Driven Active Learning Framework for Intelligent Elderly Activity Recognition" International Journal of Advance Research in Science, Management, Engineering and Technology, V1(2): Page(01-09) May-June 2026. ISSN: 3139-1141. www.ijarsmet.com. Published by Scientific and Academic Research Publishing.