The Impact of Synthetic Data on Fall Detection Application

This paper explores how synthetic sensor data generated through diffusion and GAN-based methods can enhance model generalization in fall detection tasks. Experimental results confirm that synthetic augmentation leads to improved recall and balanced classification performance.

Recommended citation: Debnath, Minakshi, Md Shahriar Kabir, Jianyuan Ni, and Anne Hee Hiong Ngu. (2024). "The Impact of Synthetic Data on Fall Detection Application." International Conference on Artificial Intelligence in Medicine (AIME), pp. 204–209. Springer Nature Switzerland.
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