A Lightweight CNN-GRU Model for Human Activity Recognition with Efficient Edge Deployment Using TFLite

Abstract

Wearable sensor-based human activity recognition (HAR) has become increasingly popular for applications in health monitoring, fitness, and smart living. But the use of deep learning models on edge devices is still challenging due to limited memory and computational power. In this paper, we develop a resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset. This architecture uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning. For deployment on the edge, the model is quantized to TensorFlow Lite (TFLite) using float16. Our experiments show that the model achieves an accuracy of 94.07%, while the size of the model is substantially smaller and suitable for deployment on edge devices. Importantly, the TFLite model maintains the same accuracy as the original model, ensuring its suitability for real-time deployment. The extensive assessment through confusion matrices, ROC curves and classification metrics confirms the effectiveness of the model across various activities. The proposed approach offers a balance between accuracy and computational efficiency, enabling real-time HAR on edge devices.