MobileHAR: A Lightweight and Efficient Human Activity Recognition Model based on Inverted Residual Inception Block

With the increasing demand for high precision and low power consumption in Human Activity Recognition (HAR) techniques, deep learning-based HAR models have emerged as the hottest research topics. Due to the excellent feature extraction and modeling abilities of deep learning models, which enable them to fit a wide variety of complex patterns. However, these models often require a large number of parameters, leading to high computational costs and longer processing time. These inherent factors pose significant challenges for resource-constraint edge devices to perform efficient HAR. To address these issues, we propose MobileHAR, which combines depthwise separable convolutions and novel Inverted Residual Inception Blocks (IRIB). This combination significantly reduces computational load and frequent memory access while maintaining high recognition accuracy. Then, we design a special class imbalance loss to supervise the model to pay more attention to imbalance classes. Finally, extensive experiments on several public datasets demonstrate that our method improves accuracy by 3.15% compared to traditional methods and requires only 0.15M parameters, which is at least four times fewer than the compared methods.