A Compact Dual-IMU Fall Detection Model for TinyML Deployment at Low Sampling Rates
Duan Luong Cong, Minh Nguyen Ngoc · 2025
Falls in the elderly pose a significant health risk, necessitating reliable, real-time detection systems suitable for efficient operation on wearable devices. This paper introduces a lightweight fall detection solution employing dual IMUs at the waist and thigh, operating at a low 20 Hz sampling rate for TinyML platform deployment. The system extracts angular velocity and Root Mean Square (RMS) features, processed by a compact Convolutional Neural Network (CNN) with integrated 1D and 2D layers to discern temporal and inter-sensor motion patterns. On a public dataset of 21,847 windows, the dual-IMU configuration achieved a compelling$\mathbf{F 1}$-score of$\mathbf{0. 9 5 2 2}$and a recall of 0.9733, significantly outperforming single-sensor approaches. Crucially, the model demonstrates high efficiency, requiring less than 130 KB of flash memory (97 KB on STM32F407) and performing inference in under$60 ~\text{ms}(36 ~\text{ms}$on STM32F407) on typical microcontrollers. These findings affirm the feasibility of accurate, responsive fall detection on resourceconstrained embedded systems without demanding high sampling frequencies or overly complex architectures.