Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification while demonstrating the explainability of the multi-class labeling of univariate EEG signals, highlighting the parts of the signals emphasized most in predicting sleep stages. We extensively evaluated our approach to the sleep-EDF dataset, and it demonstrates the highest overall accuracy of 86.8% in identifying five sleep stage classes. More importantly, we achieved the highest accuracy when classifying the crucial sleep stage N1 with the lowest number of instances, outperforming the state-of-the-art machine learning approaches by 16.3%. These results motivate us to adopt the proposed approach in clinical practice as an aid to sleep experts.

Falls are a major risk factor for older adults, increasing morbidity and healthcare costs. Video-based fall-detection systems offer crucial real-time monitoring and assistance. Yet, their deployment faces challenges such as maintaining privacy, reducing false alarms, and providing understandable outputs for healthcare providers. This paper introduces an innovative automated fall-detection framework that includes a Gaussian blur module for privacy preservation, an OpenPose module for precise pose estimation, a short-time Fourier transform (STFT) module to capture frames with significant motion selectively, and a computationally efficient one-dimensional convolutional neural network (1D-CNN) classification module designed to classify these frames. Additionally, integrating a gradient-weighted class activation mapping (GradCAM) module enhances the system’s explainability by visually highlighting the movement of the key points, resulting in classification decisions. Modular flexibility in our system allows customization to meet specific privacy and monitoring needs, enabling the activation or deactivation of modules according to the operational requirements of different healthcare settings. This combination of STFT and 1D-CNN ensures fast and efficient processing, which is essential in healthcare environments where real-time response and accuracy are vital. We validated our approach across multiple datasets, including the Multiple Cameras Fall Dataset (MCFD), the UR fall dataset, and the NTU RGB+D Dataset, which demonstrates high accuracy in detecting falls and provides the interpretability of results.

This paper presents a human activity recognition framework tailored for healthcare applications, emphasizing the essential balance between accuracy and interpretability required for medical monitoring. The model utilizes MediaPipe to capture the complex dynamics of human movements and introduce an interpretable feature reduction function. This method improves traditional dimensionality reduction techniques like principal component analysis. Our feature engineering is based on the importance of feature permutations; it selectively retains salient features, thus enhancing the interpretability essential for the medical domain. We validated our method on the “NTU RGB+D” dataset; it improves the recognition accuracy for a range of human activities that may be relevant for elderly care. However, the recognition of subtler activities like neck pain and headaches requires further investigation. This study underscores the potential to advance patient monitoring and sets the stage for its expanded application in various medical contexts.