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 mapping (1D Grad-CAM) and local interpretable model-agnostic explanations (LIME) were used to interpret how the model made its predictions.

Results: The model on the FallAllD dataset achieved an accuracy of 97.81%, a recall of 98.55%, and an F1-score of 97.88%, with an area under the receiver operating characteristic curve of 99.33%. With a size of just 0.312 MB and an inference time of 7.07 ms, LiteFallNet combines strong performance with efficiency. These attributes make it highly suitable for deployment in real-time, resource-constrained environments.

Conclusion: LiteFallNet offers a privacy-preserving and real-time solution for fall detection. Its accuracy, transparency, and lightweight design make it suitable for smart homes, eldercare facilities, and wearable health technologies.