Paediatric Activity Anomaly Detection using CNN-XGBoost for Early Intervention and Patient Safety

Abstract

The development of a robust anomaly detection system capable of distinguishing between normal and abnormal activities in paediatric care settings is of paramount importance for early intervention and patient safety. This endeavor presents substantial challenges due to the unique characteristics of paediatric activities, making it necessary to employ a combination of Convolutional Neural Networks (CNNs) and XGBoost to effectively address these complexities. The initial step involves the assembly of a meticulously annotated dataset comprising video sequences encompassing a broad spectrum of paediatric activities, both normal and abnormal, with a strong emphasis on diversity and representativeness. This dataset is subjected to rigorous preprocessing to ensure consistency and quality. This research employs a Kalman filter as a pre-processing step. This filter helps to reduce noise and enhance the quality of the video data. By smoothing and stabilizing the trajectories of objects or subjects within the video frames, the Kalman filter provides a cleaner input for subsequent stages of the process. CNN employed for feature extraction from video frames, capturing both spatial and temporal cues. To account for the temporal dimension inherent in video data, by ensuring that the model can capture nuanced patterns in paediatric activities. The accuracy of the proposed method is 88%. The extracted features or sequences are input into an XGBoost classifier, designed as a binary classification task to differentiate between normal and abnormal activities. Robust model evaluation is conducted on validation and test datasets using appropriate performance metrics, with continuous feedback loops established with healthcare professionals and caregivers to fine-tune and optimize the model's performance.