Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years.At present, the research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method.Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on singleobject activity recognition, which is not suitable for multi-object scenarios.To address these issues, we propose DeepMultiple, a novel tag non-attachment activity recognition model for multiobject.The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally ProbSparse is used to optimize the vanilla Transformer-based Encoder to enhance the activity recognition ability.We deployed a single reader and antenna for multi-object activity tracking during the experiments to reduce deployment difficulties.Extensive experimental results show that DeepMultiple can recognize ten types of multi-object activities with 98.12% precision under different challenging settings, which has excellent performance compared with several state-of-the-art methods.

Wireless sensing techniques for Human Activity Recognition (HAR) have been widely studied in recent years. At present, research on HAR based on Radio Frequency Identification (RFID) is changing from the tag attachment method to the tag non-attachment method. Affected by multipath, the current solutions in tag non-attachment scenarios mainly focus on single-object activity recognition, which is not suitable for multi-object scenarios. To address these issues, we propose STMultiple, a novel tag non-attachment activity recognition model for multi-object. The model first preprocesses the raw signal with filter and phase calibration, then it applies dilated convolution in the frequency domain to extract multi-object activity features, finally the feature pyramid structure and ProbSparse are used to optimize the vanilla Transformer-Encoder to enhance the activity recognition ability. Extensive experiments show that STMultiple can achieve recognition accuracy of up to 97.93% and down to about 90% in challenging environments ranging from two to five users, which has excellent performance compared to several state-of-the-art methods.