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 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.