Fall detection system using tabular GAN for data augmentation with integration of isolation forest model

Effective Fall Detection Systems (FDS) are essential to minimize the risk of severe injuries among the elderly. However, the limited availability and imbalanced nature of fall detection data pose significant challenges to developing accurate models. This paper proposes a novel approach to enhance fall detection accuracy by using synthetic data generated through Tabular Generative Adversarial Network (GAN), combined with Isolation Forest and Autoencoder models. The dataset was augmented by factors of 5 and 10, and the models were evaluated using the area under the curve-receiver operating characteristic (AUC-ROC) and area under the curve–precision recall (AUC-PR) metrics. Notably, the Isolation Forest model improved from an AUC-ROC of 0.49 and AUC-PR of 0.43 (without augmentation) to 0.59 and 0.63, respectively, with 5x augmentation. Similarly, the Autoencoder showed an increase from 0.4 (AUC-ROC) and 0.2 (AUC-PR) to 0.5 and 0.54 with the same augmentation. These results demonstrate the effectiveness of synthetic data in improving anomaly detection performance. The findings suggest that advanced data augmentation techniques significantly improve FDS, thereby enhancing safety and quality of life for the vulnerable population.