Activity classification of the elderly based on lightweight convolutional neural networks

Abstract

Accurate implementation of action classification for the elderly on lightweight convolutional neural networks benefits resource-limited embedded and mobile devices in the healthcare industry. The study proposes a lightweight convolutional neural network model called mD-MobileNet. The micro-Doppler feature spectrograms of 106 elderly people were studied as a dataset. Transfer learning methods were used to train the proposed model, and three lightweight convolutional neural networks (MobileNetV3-Small, ShuffleNetV2, and EfficientNet-B0) were compared using the same training method. All of these models were able to correctly classify various actions. By comparison, mD-MobileNet gave the best classification results. mD-MobileNet’s Top-1 Accuracy reached 96.1% while Marco F1 was 96.30. By comparing the results with Grad-CAM’s visualization and analyzing them in conjunction with its network structure features, it was determined that mD-MobileNet has the best local perception with the least number of model parameters and the highest accuracy rate compared to other models.