RFID-based Human Activity Recognition Using Multimodal Convolutional Neural Networks

Recognition of human activities is crucial for enhancing safety, efficiency, and productivity within industrial and factory automation settings. This paper introduces a model for human activity recognition that leverages battery-less body-worn reflective antenna components. We perform preprocessing on both the backscattered phase and Received Signal Strength (RSS) signals. Independently and simultaneously, we extract features from phase and RSS signals using a feature extractor implementing a convolutional neural network (CNN). These features are then concatenated and fed into a fully connected (FC) layer employing the rectified linear unit (ReLU) activation function, followed by another FC layer utilizing a softmax function. This model, which merges extracted features from both phase and RSS, is termed late fusion model. We show that late fusion yields better performance than combining phase and RSS signals before feeding them into the neural network. By employing battery-free body-worn Radio frequency identification (RFID) tags, we surpass existing models, achieving an accuracy of 97.5% in recognizing five activities.