The Impact of Synthetic Data on Fall Detection Application

Lack of real-world data in clinical fields poses a major obstacle for training deep learning models. Using data augmentation can increase data volume, making the training of deep learning models more effective. This paper aims to investigate different techniques for generating realistic multivariate synthetic fall data, addressing the challenge of limited fall data availability. We experimented with three traditional time series data augmentation techniques, a generative AI approach with diffusion, and extraction of data from public video recordings of older adults falling. We evaluated the effectiveness of the generated data with both an LSTM model trained offline and using the SmartFall App running the LSTM model in real-time. Initial results indicate a 7–10% increase in the F1-score for the fall detection model when trained with additional data generated through the diffusion method during offline evaluation and a notable improvement of 24% was observed with the real-time evaluation of the model.