Unobtrusive Elderly Action Recognition with Transitions Using CNN-RNN

This study addresses the efficient recognition of elderly people's daily actions, emphasizing transition states, using privacy-preserving depth data and deep learning algorithms. Stereo-depth cameras collect data from an elder care center, ensuring privacy by capturing only depth information without revealing identifiable details. The research investigates spatial and temporal features in movement patterns by employing a Convolutional Neural Network (CNN) for transfer learning on segmented person image sequences to extract spatial features, while a Recurrent Neural Network (RNN) decoder extracts temporal features. The proposed study evaluated various CNN and RNN integrated architectures, assessing algorithmic performance on real-world data from three elderly participants. Experimental outcomes reveal the best model achieving 95% overall accuracy for all actions and an average accuracy of over 80% for classifying transition states. Beyond accuracy, comprehensive evaluation includes precision, recall, and F1-score, offering a thorough assessment of the developed algorithm's practical effectiveness on real-world data.