Fall Event Classification

Fall event classification aims to automatically identify and categorize different types of falls using sensor data, primarily focusing on improving accuracy and handling noisy or incomplete data. Current research emphasizes the use of human skeleton data extracted from video, mitigating privacy concerns while providing robust features, often employing deep neural networks with techniques like two-stage classification or ensemble methods to improve performance in the presence of noisy labels. This research is crucial for developing effective fall detection systems in healthcare, enabling timely intervention and improved care for the elderly and other vulnerable populations.