Performance Analysis of Data Augmentation Approaches for Improving Wrist-Based Fall Detection System

The aging of society is a global concern nowadays. Falls and fall-related injuries can influence the elderly's daily living, including physical damage, psychological effects, and financial problems. A reliable fall detection system can trigger an alert immediately when a fall event happens to reduce the adverse effects of falls. Notably, the wrist-based fall detection system provides the most acceptable placement for the elderly; however, the performance is the worst due to the complicated hand movement modeling. Many works recently implemented deep learning technology on wrist-based fall detection systems to address the worst, but class imbalance and data scarcity issues occur. In this study, we analyze different data augmentation methodologies to enhance the performance of wrist-based fall detection systems using deep learning technology. Based on the results, the conditional diffusion model is an ideal data augmentation approach, which improves the F1 score by 6.58% when trained with only 25% of the actual data, and the synthetic data maintains a high quality.