LiteFallNet: A lightweight deep learning model for efficient real-time fall detection.

ABSTRACT:

Objective

This study introduces LiteFallNet, a lightweight and interpretable deep learning model for real-time fall detection using only inertial sensor data. It aims to overcome key limitations in current systems, including high computational demands, latency, and privacy concerns, while delivering accurate and reliable performance.

Methods

LiteFallNet integrates a Gated Recurrent Unit (GRU) layer, a Temporal Convolutional Network (TCN) block, depthwise separable convolutions, and a Squeeze-and-Excitation (SE) block to efficiently extract temporal features from tri-axial accelerometer, gyroscope, and magnetometer signals. The model was trained and evaluated on the FallAllD and the UMAFall datasets. To enhance transparency, one-dimensional gradient-weighted class activation

SUBMITTER: Owusu E 

PROVIDER: S-EPMC12515292 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature